In the process of community detecting , this algorithm defines the concept of the similarity coefficient . And based on the similarity coefficient , this algorithm determines which community the nodes belong to , as well as adjusts the results of community detecting . The experiment confirms that the algorithm in this paper can not only guarantee the feasibility but also improve the quality of community detecting . A new strategy for probabilistic graphical modeling is developed that draws parallels to community detection analysis . The method jointly estimates an undirected graph and homogenous communities of nodes . Despite their performance on dynamic complex networks , their formulations lack complete reflection of sufficient community detection model . Our framework , then , models the evolutionary clustering problem by hypothesizing that it should not depart too much from the community detection problem . To support this claim , an alternate decomposition perspective is proposed by projecting the problem , as a multi-objective optimization problem , in the light of snapshot and temporal of both intra- and inter-community scores . By comparing the existing community detection algorithms , the label propagation algorithm ( LPA ) shows prominent operation speed and qualifies near linear time complexity . However , original LPA algorithm only uses the topological structure to guide the community detection process , failing to improve the quality of community detection when extra information offered . In this paper , we combine the prior information with topological structure to guide the community detection process . We explore the mathematical background of this phenomenon and several of its potential applications . A special focus is laid on the possibilities and limitations pertaining to the exploitation of the phenomenon for community detection purposes . We show that SCD is to detect communities with an unprecedented quality , being its execution time faster than most of existing proposals , being able to process billion edge graphs in a few hours This thesis also includes a statistical study about the structural characteristics of the meta-groups found in several real graphs , comparing these to graph from two different synthetic graph generators . We show that communities produced by a synthetic graph generator commonly used in community detection research are very dissimilar to those found in real graphs . Finally , this thesis includes a study on how to implement a triangle counting algorithm on a modern many core architecture , more concretely the Intel Single Chip Cloud Computer ( Intel SCC ) . We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan ( NG ) modularity function . The NG function serves as a cluster validity functional in community detection studies . The input data is an undirected weighted graph that represents , e.g. , a social network . is a vertex chosen at random from the neighbors of vertex v k . Random walks have been used as a similarity measure for a variety of problems in content recommendation [ reference ] and community detection [ reference ] . Here we demonstrate how this ‘ metadata ' can be used to improve our understanding of network structure . We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone . Crucially , the method does not assume that the metadata are correlated with the communities we are trying to find . Community detection in signed complex networks is a challenging research problem aiming at finding groups of entities having positive connections within the same cluster and negative relationships between different clusters . Most of the proposed approaches have been developed for networks having only positive edges . There is a surge of community detection study on complex network analysis in recent years , since communities often play important roles in network systems . However , many real networks have more complex overlapping community structures . There is a surge of community detection study on complex network analysis in recent years , since communities often play important roles in network systems . However , many real networks have more com ... In addition to community-level evaluation , we propose a framework for evaluating algorithms ' ability to detect overlapping nodes , which helps to assess overdetection and underdetection . After considering community-level detection performance measured by normalized mutual information , the Omega index , and node-level detection performance measured by F-score , we reached the following conclusions . For low overlapping density networks , SLPA , OSLOM , Game , and COPRA offer better performance than the other tested algorithms . We test these algorithms on both synthetic and real-world network datasets . The experimental results show that the local community detection algorithms implemented in our framework are better at detecting local community compared with related algorithms . That means the performance of discovering local community would be largely improved by using good node similarity measurements . In this paper , we not only define Local community modularity and Tightness between local communities for directed weighted networks , but also realize a distributed algorithm that detects overlapping local community in networks . The algorithm is divided into two parts , initial local community detection and similar communities merging . The core of the algorithm is to agglomerate node which causes the greatest local modularity increments for local community , and by iteratively to merge similar communities that have the maximum tightness . Local community detection , which aims to find a target community containing a set of query nodes , has recently drawn intense research interest . The existing local community detection methods usually assume all query nodes are from the same community and only find a single target community . In real-world social networks , global information ( e.g. , the number of nodes and the connections between them ) is incomplete or expensive to acquire ; therefore , local community detection becomes especially important . Local community detection is used to identify the local community to which the given starting node belongs according to local information . Such a definition generalizes the original modularity in ( Newman & Girvan , 2004 ) and recovers the stability in ( Lambiotte , 2010 ; Delvenne et al. , 2010 ) as special cases . For the local community detection problem , we develop agglomerative algorithms that guarantee the community strength of the detected local community . There are two nice features of our local community detection algorithms : ( i ) local search : only nodes in the neighboring set need to be explored , and ( ii ) recursive update : the relative centralities can be efficiently updated by using recursive formulae . In complex network analysis , the local community detection problem is getting more and more attention . Because of the difficulty to get complete information of the network , such as the World Wide Web , the local community detection has been proposed by researcher . We have devised this method inspired by possible mechanisms for stable propagation of neuronal activities in neural networks . To demonstrate the effectiveness of our method , local detection of communities in synthetic benchmark networks and real social networks is examined . The community structure detected by our method is perfectly consistent with the correct community structure of these networks . Community structure is a hallmark of a variety of social , biological and engineering networks , and development of effective and efficient methods for detecting communities in networks has been a central issue of network science . We put forward a method of local detection of communities inspired by the cell assembly hypothesis , which has been longstanding and prevailing in neuroscience . The hypothesis states that : Neurons coding a specific item tend to be mutually connected , thus forming a ‘ cell assembly ’ ; memory recall of the item is associated with reverberative activation of neurons comprising the cell assembly . Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way . Conductance is a popular objective function used in many algorithms for local community detection . Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way . Conductance is a popular objective function used in ... Based on the definition of local spectral subspace , we propose a novel approach called LOSP for local overlapping community detection . Using the power method for a few steps , LOSP finds an approximate invariant subspace , which depicts the embedding of the local neighborhood structure around the seeds of interest . Given an overlapping node that belongs to multiple communities , the problem is to find communities to which it belongs according to local information . We propose a framework for local overlapping community detection . The framework has three steps . In this paper we propose a new algorithm called Community Detection algorithm for mining interesting communities or groups in a Campus Mobile Social Network ( CMSN ) . The proposal algorithm is composed of two main components , an algorithm for community partition and an algorithm for selecting small communities to combine into a big community . Empirical studies on a campus mobile social network show that performance of the proposal algorithm is better than the state-of-the-art Newman Clustering algorithm for mining community in CMSN . Community partition is an important problem in many areas such as biology network , social network . The objective of this problem is to analyse the relationships among data via the network topology . In this paper , we consider the community partition problem under IC model in social networks . We formulate the problem as a combinatorial optimization problem which aims at partitioning a given social network into disjoint M communities . We also devise a simply greedy to solve the original objective function and apply the sandwich approximation framework to it to guarantee a data dependent approximation factor . Finally , our algorithms are evaluated on two real data sets , which clearly verifies the effectiveness of our method in community partition problem , as well as the advantage of our method against the other methods . Abstract The methods of community partition in nowadays mainly focus on using the topological links , and consider little of the content-based information between users . In the paper we analyze the content similarity between users by the SimHash method , and compute the content-based weight of edges so as to attain a more reasonable community partition . The small-world and scalefree character can be used in community detection . In this paper , we propose a folding contraction method ( FCM ) for community partition detection based on scale-free networks feature . And a framework of this method has been provided . Community partition is of great importance in social networks because of the rapid increasing network scale , data and applications . We consider the community partition problem under LT model in social networks , which is a combinatorial optimization problem that divides the social network to disjoint $ m$ communities . Our goal is to maximize the sum of influence propagation through maximizing it within each community . By adjusting the weights of different base kernel matrices , a new kernel matrix is constructed using linear combination of those matrices . To partition networks whose number of communities are known in advance , we derive a new kernel matrix which forms a basis for community partitioning . We further propose a novel robust multiple-kernel combination-based fuzzy clustering algorithm . To this end , this paper aims to reveal the underlying process and reasons for topic formation and development ( TFD ) . Along this line , based on community partitioning in social networks , a core-group model is proposed to explain the dynamics and to segment topic development . This model is inspired by the cell division mechanism in biology . By distinguishing the meeting situation of mobile users , the meeting times and the duration of contacts are calculated , based on which a social relationship matrix is obtained . On this basis , the existing complex network community detection algorithms are applied for community partitioning based on social relationships , the community size , modularity and time complexity are analyzed and compared , which provides a guidance for choosing different community detection algorithm for trajectory data in different application scenarios . The global residue community structure and the local residue interactions are two important coexisting factors imposing significant effects on low-frequency normal modes . In this work , an algorithm for community structure partition is proposed by integrating Miyazawa-Jernigan empirical potential energy as edge weight . A sensitivity parameter is defined to measure the effect of local residue interaction on low-frequency movement . Finally we partition the network community based on snapshots with stable structures before and after the variation points . According to experimental results , our proposed probabilistic snapshot model is effective for network events detection and network community partition . Abstract In this paper , we proposed a novel two-stage optimization method for network community partition , which is based on inherent network structure information . The introduced optimization approach utilizes the new centrality measure of both network links and vertices to construct the key affinity description of the given network , while the direct similarities between graph nodes or nodal features are not available to obtain the classical affinity matrix . For the characteristics of topology structure attributes and edges ' signed attributes in signed networks , a novel method of Signed Network Community Partition is proposed . Firstly , based on signed attributes , the initial center vertex is selected as random walk starting vertex . Due to the exponentially increasing amount of social network applications , a fast and accurate method is necessary for community partition in social networks . In view of this , we investigate the social community partition problem from the perspective of influence propagation , which is one of the most important features of social communication . MethodsWe formulate social community partition as a combinatorial optimization problem that aims at partitioning a social network into K disjoint communities such that the sum of influence propagation within each community is maximized . When K=2 we develop an optimal algorithm that has a provable performance guarantee for a class of influence propagation models . Social network partitioning has become a very important function . One objective for partitioning is to identify interested communities to target for marketing and advertising activities . Recently there is an increasing need for online community analysis on large scale graphs . Community search ( CS ) , which can retrieve communities efficiently on a query request , has received significant research attention . Typically , the spatio-temporal patterns arising from such activity can be analyzed in order to determine the latent community structure in the underlying data . In this paper , we will examine the problem of online community detection from the location data collected from such social sensing applications in real time . Such data brings numerous challenges associated with it , in that they can be of a relatively large scale , and can be extremely noisy from the perspective of both data representation and analysis . In this context , this paper proposes an approach for analysing dynamic social networks , more precisely for Twitter 's network . Our approach relies on two complementary steps : ( i ) an online community identification based on a dynamic community detection algorithm called Dyci . The main idea of Dyci is to track whether a connected component of the weighted graph becomes weak over time , in order to merge it with the " dominant " neighbour community . By using the parallel manner and sophisticated data structures , its running time is essentially fast , O(k^m^a^x(k^m^a^x+logk^m^a^x ) ) . Finally , we propose an evaluation metric , which is based on the reliable ground truths , for online network community detection . In the experimental work , we evaluate our method using real datasets and compare our approach with several previous methods ; the results show that our method is more effective and accurate in find potential online communities . With each new definition , there is a need to study in what applications or context such a definition is appropriate , and develop algorithms and complexity results for the computation of these clusterings . This thesis studies problems related to graph clustering , motivated by the social networking problem of community detection . One main contribution of this thesis is a new definition of a specific kind of family-like community , accompanied by theoretical and computational justifications .
In the process of community detecting , this algorithm defines the concept of the similarity coefficient . And based on the similarity coefficient , this algorithm determines which community the nodes belong to , as well as adjusts the results of community detecting . The experiment confirms that the algorithm in this paper can not only guarantee the feasibility but also improve the quality of community detecting .
A new strategy for probabilistic graphical modeling is developed that draws parallels to community detection analysis . The method jointly estimates an undirected graph and homogenous communities of nodes .
Despite their performance on dynamic complex networks , their formulations lack complete reflection of sufficient community detection model . Our framework , then , models the evolutionary clustering problem by hypothesizing that it should not depart too much from the community detection problem . To support this claim , an alternate decomposition perspective is proposed by projecting the problem , as a multi-objective optimization problem , in the light of snapshot and temporal of both intra- and inter-community scores .
By comparing the existing community detection algorithms , the label propagation algorithm ( LPA ) shows prominent operation speed and qualifies near linear time complexity . However , original LPA algorithm only uses the topological structure to guide the community detection process , failing to improve the quality of community detection when extra information offered . In this paper , we combine the prior information with topological structure to guide the community detection process .
We explore the mathematical background of this phenomenon and several of its potential applications . A special focus is laid on the possibilities and limitations pertaining to the exploitation of the phenomenon for community detection purposes .
We show that SCD is to detect communities with an unprecedented quality , being its execution time faster than most of existing proposals , being able to process billion edge graphs in a few hours This thesis also includes a statistical study about the structural characteristics of the meta-groups found in several real graphs , comparing these to graph from two different synthetic graph generators . We show that communities produced by a synthetic graph generator commonly used in community detection research are very dissimilar to those found in real graphs . Finally , this thesis includes a study on how to implement a triangle counting algorithm on a modern many core architecture , more concretely the Intel Single Chip Cloud Computer ( Intel SCC ) .
We discuss a new formulation of a fuzzy validity index that generalizes the Newman-Girvan ( NG ) modularity function . The NG function serves as a cluster validity functional in community detection studies . The input data is an undirected weighted graph that represents , e.g. , a social network .
is a vertex chosen at random from the neighbors of vertex v k . Random walks have been used as a similarity measure for a variety of problems in content recommendation [ reference ] and community detection [ reference ] .
Here we demonstrate how this ‘ metadata ' can be used to improve our understanding of network structure . We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone . Crucially , the method does not assume that the metadata are correlated with the communities we are trying to find .
Community detection in signed complex networks is a challenging research problem aiming at finding groups of entities having positive connections within the same cluster and negative relationships between different clusters . Most of the proposed approaches have been developed for networks having only positive edges .
There is a surge of community detection study on complex network analysis in recent years , since communities often play important roles in network systems . However , many real networks have more complex overlapping community structures .
There is a surge of community detection study on complex network analysis in recent years , since communities often play important roles in network systems . However , many real networks have more com ...
In addition to community-level evaluation , we propose a framework for evaluating algorithms ' ability to detect overlapping nodes , which helps to assess overdetection and underdetection . After considering community-level detection performance measured by normalized mutual information , the Omega index , and node-level detection performance measured by F-score , we reached the following conclusions . For low overlapping density networks , SLPA , OSLOM , Game , and COPRA offer better performance than the other tested algorithms .
We test these algorithms on both synthetic and real-world network datasets . The experimental results show that the local community detection algorithms implemented in our framework are better at detecting local community compared with related algorithms . That means the performance of discovering local community would be largely improved by using good node similarity measurements .
In this paper , we not only define Local community modularity and Tightness between local communities for directed weighted networks , but also realize a distributed algorithm that detects overlapping local community in networks . The algorithm is divided into two parts , initial local community detection and similar communities merging . The core of the algorithm is to agglomerate node which causes the greatest local modularity increments for local community , and by iteratively to merge similar communities that have the maximum tightness .
Local community detection , which aims to find a target community containing a set of query nodes , has recently drawn intense research interest . The existing local community detection methods usually assume all query nodes are from the same community and only find a single target community .
In real-world social networks , global information ( e.g. , the number of nodes and the connections between them ) is incomplete or expensive to acquire ; therefore , local community detection becomes especially important . Local community detection is used to identify the local community to which the given starting node belongs according to local information .
Such a definition generalizes the original modularity in ( Newman & Girvan , 2004 ) and recovers the stability in ( Lambiotte , 2010 ; Delvenne et al. , 2010 ) as special cases . For the local community detection problem , we develop agglomerative algorithms that guarantee the community strength of the detected local community . There are two nice features of our local community detection algorithms : ( i ) local search : only nodes in the neighboring set need to be explored , and ( ii ) recursive update : the relative centralities can be efficiently updated by using recursive formulae .
In complex network analysis , the local community detection problem is getting more and more attention . Because of the difficulty to get complete information of the network , such as the World Wide Web , the local community detection has been proposed by researcher .
We have devised this method inspired by possible mechanisms for stable propagation of neuronal activities in neural networks . To demonstrate the effectiveness of our method , local detection of communities in synthetic benchmark networks and real social networks is examined . The community structure detected by our method is perfectly consistent with the correct community structure of these networks .
Community structure is a hallmark of a variety of social , biological and engineering networks , and development of effective and efficient methods for detecting communities in networks has been a central issue of network science . We put forward a method of local detection of communities inspired by the cell assembly hypothesis , which has been longstanding and prevailing in neuroscience . The hypothesis states that : Neurons coding a specific item tend to be mutually connected , thus forming a ‘ cell assembly ’ ; memory recall of the item is associated with reverberative activation of neurons comprising the cell assembly .
Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way . Conductance is a popular objective function used in many algorithms for local community detection .
Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way . Conductance is a popular objective function used in ...
Based on the definition of local spectral subspace , we propose a novel approach called LOSP for local overlapping community detection . Using the power method for a few steps , LOSP finds an approximate invariant subspace , which depicts the embedding of the local neighborhood structure around the seeds of interest .
Given an overlapping node that belongs to multiple communities , the problem is to find communities to which it belongs according to local information . We propose a framework for local overlapping community detection . The framework has three steps .
In this paper we propose a new algorithm called Community Detection algorithm for mining interesting communities or groups in a Campus Mobile Social Network ( CMSN ) . The proposal algorithm is composed of two main components , an algorithm for community partition and an algorithm for selecting small communities to combine into a big community . Empirical studies on a campus mobile social network show that performance of the proposal algorithm is better than the state-of-the-art Newman Clustering algorithm for mining community in CMSN .
Community partition is an important problem in many areas such as biology network , social network . The objective of this problem is to analyse the relationships among data via the network topology . In this paper , we consider the community partition problem under IC model in social networks . We formulate the problem as a combinatorial optimization problem which aims at partitioning a given social network into disjoint M communities .
We also devise a simply greedy to solve the original objective function and apply the sandwich approximation framework to it to guarantee a data dependent approximation factor . Finally , our algorithms are evaluated on two real data sets , which clearly verifies the effectiveness of our method in community partition problem , as well as the advantage of our method against the other methods .
Abstract The methods of community partition in nowadays mainly focus on using the topological links , and consider little of the content-based information between users . In the paper we analyze the content similarity between users by the SimHash method , and compute the content-based weight of edges so as to attain a more reasonable community partition .
The small-world and scalefree character can be used in community detection . In this paper , we propose a folding contraction method ( FCM ) for community partition detection based on scale-free networks feature . And a framework of this method has been provided .
Community partition is of great importance in social networks because of the rapid increasing network scale , data and applications . We consider the community partition problem under LT model in social networks , which is a combinatorial optimization problem that divides the social network to disjoint $ m$ communities . Our goal is to maximize the sum of influence propagation through maximizing it within each community .
By adjusting the weights of different base kernel matrices , a new kernel matrix is constructed using linear combination of those matrices . To partition networks whose number of communities are known in advance , we derive a new kernel matrix which forms a basis for community partitioning . We further propose a novel robust multiple-kernel combination-based fuzzy clustering algorithm .
To this end , this paper aims to reveal the underlying process and reasons for topic formation and development ( TFD ) . Along this line , based on community partitioning in social networks , a core-group model is proposed to explain the dynamics and to segment topic development . This model is inspired by the cell division mechanism in biology .
By distinguishing the meeting situation of mobile users , the meeting times and the duration of contacts are calculated , based on which a social relationship matrix is obtained . On this basis , the existing complex network community detection algorithms are applied for community partitioning based on social relationships , the community size , modularity and time complexity are analyzed and compared , which provides a guidance for choosing different community detection algorithm for trajectory data in different application scenarios .
The global residue community structure and the local residue interactions are two important coexisting factors imposing significant effects on low-frequency normal modes . In this work , an algorithm for community structure partition is proposed by integrating Miyazawa-Jernigan empirical potential energy as edge weight . A sensitivity parameter is defined to measure the effect of local residue interaction on low-frequency movement .
Finally we partition the network community based on snapshots with stable structures before and after the variation points . According to experimental results , our proposed probabilistic snapshot model is effective for network events detection and network community partition .
Abstract In this paper , we proposed a novel two-stage optimization method for network community partition , which is based on inherent network structure information . The introduced optimization approach utilizes the new centrality measure of both network links and vertices to construct the key affinity description of the given network , while the direct similarities between graph nodes or nodal features are not available to obtain the classical affinity matrix .
For the characteristics of topology structure attributes and edges ' signed attributes in signed networks , a novel method of Signed Network Community Partition is proposed . Firstly , based on signed attributes , the initial center vertex is selected as random walk starting vertex .
Due to the exponentially increasing amount of social network applications , a fast and accurate method is necessary for community partition in social networks . In view of this , we investigate the social community partition problem from the perspective of influence propagation , which is one of the most important features of social communication . MethodsWe formulate social community partition as a combinatorial optimization problem that aims at partitioning a social network into K disjoint communities such that the sum of influence propagation within each community is maximized . When K=2 we develop an optimal algorithm that has a provable performance guarantee for a class of influence propagation models .
Social network partitioning has become a very important function . One objective for partitioning is to identify interested communities to target for marketing and advertising activities .
Recently there is an increasing need for online community analysis on large scale graphs . Community search ( CS ) , which can retrieve communities efficiently on a query request , has received significant research attention .
Typically , the spatio-temporal patterns arising from such activity can be analyzed in order to determine the latent community structure in the underlying data . In this paper , we will examine the problem of online community detection from the location data collected from such social sensing applications in real time . Such data brings numerous challenges associated with it , in that they can be of a relatively large scale , and can be extremely noisy from the perspective of both data representation and analysis .
In this context , this paper proposes an approach for analysing dynamic social networks , more precisely for Twitter 's network . Our approach relies on two complementary steps : ( i ) an online community identification based on a dynamic community detection algorithm called Dyci . The main idea of Dyci is to track whether a connected component of the weighted graph becomes weak over time , in order to merge it with the " dominant " neighbour community .
By using the parallel manner and sophisticated data structures , its running time is essentially fast , O(k^m^a^x(k^m^a^x+logk^m^a^x ) ) . Finally , we propose an evaluation metric , which is based on the reliable ground truths , for online network community detection . In the experimental work , we evaluate our method using real datasets and compare our approach with several previous methods ; the results show that our method is more effective and accurate in find potential online communities .
With each new definition , there is a need to study in what applications or context such a definition is appropriate , and develop algorithms and complexity results for the computation of these clusterings . This thesis studies problems related to graph clustering , motivated by the social networking problem of community detection . One main contribution of this thesis is a new definition of a specific kind of family-like community , accompanied by theoretical and computational justifications .
An Extension to Hub Algorithm Based on Triangular Loop Community-Based Group Graphical Lasso A new multi-objective evolutionary framework for community mining in dynamic social networks Using Prior Knowledge for Community Detection by Label Propagation Algorithm Stochastic graph Voronoi tessellation reveals community structure Scalable community detection for social networks A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks DeepWalk: online learning of social representations Structure and inference in annotated networks Community mining in signed networks: A multiobjective approach A link clustering based overlapping community detection algorithm A link clustering based overlapping community detection algorithm Overlapping community detection in networks: The state-of-the-art and comparative study A Unified Framework of Lightweight Local Community Detection for Different Node Similarity Measurement Overlapping Local Community Detection in Directed Weighted Networks Memory-based random walk for multi-query local community detection Multiscale Local Community Detection in Social Networks Relative centrality and local community detection Local Community Detection Based on Bridges Ideas Local Detection of Communities by Neural-Network Dynamics Local detection of communities by an analogy to memory recall in the brain Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means Local network community detection with continuous optimization of conductance and weighted kernel K-means Detecting Overlapping Communities from Local Spectral Subspaces Local Overlapping Community Detection Mining community in social network using call detail records Influence-based Community Partition with Sandwich Method for Social Networks The Research of Weighted Community Partition based on SimHash A Folding Contraction Method for Community Partition Detection Continuous Influence-based Community Partition for Social Networks Multiple-kernel combination fuzzy clustering for community detection Topic formation and development: a core-group evolving process Social Relationship Modeling and Community Detection Based on Trajectory Data The Relationship Between Low-Frequency Motions and Community Structure of Residue Network in Protein Molecules Events detection and community partition based on probabilistic snapshot for evolutionary social network Variational Community Partition with Novel Network Structure Centrality Prior Research of Community Partition Based on the Modularity in Signed Network Influence-based community partition for social networks Detecting Local Communities within a Large Scale Social Network Using Mapreduce An Effective Method for Community Search in Large Directed Attributed Graphs Online community detection in social sensing On the visualization of the detected communities in dynamic networks: A case study of Twitter's network A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network Utilizing graph classes for community detection in social and complex networks
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2014 2020 2016 2014 2017 2016 2013 2014 2016 2013 2013 2013 2013 2017 2014 2020 2019 2015 2016 2013 2013 2016 2016 2015 2020 2012 2020 2013 2017 2020 2020 2014 2019 2018 2017 2019 2015 2014 2014 2017 2013 2016 2013 2015
CyberC KDD '14 ASONAM SMP CSE DMBD ICANN ICDM FSKD ITQM HPCC/SmartCity/DSS ICDS MSN WSDM
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