Finally all distances among linked nodes will converge into a stable pattern , and communities can be intuitively identified . Thanks to the dynamic viewpoint of community detection , Attractor has several potential attractive properties : ( a ) Attractor provides an intuitive solution to analyze the community structure of a network , and faithfully captures the natural communities ( with high quality ) . ( b ) Owing to its time complexity $ O(|E|)$ , Attractor allows finding communities on large networks . While bipartite networks exhibit community structure like their unipartite counterparts , existing approaches to bipartite community detection have drawbacks , including implicit parameter choices , loss of information through one-mode projections , and lack of interpretability . Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model , which explicitly includes vertex type information and may be trivially extended to k-partite networks . This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results . An efficient way to detect a community corresponding to a specific topic can greatly help users to obtain useful information . However , existing community detecting algorithms are all based on pages as units , the topic drift phenomenon becomes the inherent problem of each existing community detecting algorithm since the web pages always have multiple topics in content . This paper puts forward a new community detecting algorithm based on blocks as units , which combines the page dividing algorithm with the primary community detecting algorithm , overcoming the defect in the existing community detecting algorithms . Such methods are only designed with links of the underlying social network . However , with the development of recent applications , rich edge content can be available to give another view to the community detection process . In this study , we focus on improving community detection with the edge content in social networks . We present the results of a community detection analysis of the Wikipedia graph . Distinct communities in Wikipedia contain semantically closely related articles . 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 ) . It is necessary to unveil the community structure of online participants to overcome this limitation . Previous community detection studies usually account for the structural factor of social networks to build their models . Based on the affect theory of social exchange , this research argues that emotions involved in social interactions should be considered in the community detection process . In this paper , we study how to incorporate node diversity into influence maximization ( IM ) . We consider diversity as a reverse measure of the average similarity between selected nodes , which can be specified using node embedding or community detection results . Our goal is to identify a set of nodes which are simultaneously influential and diverse . 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 ] . The current paper reports about the development of a cluster ensemble based technique combining the concepts of multiobjective optimization and deep-learning models for gene clustering where some additional protein-protein interaction information are utilized for generating the consensus partitioning . The first approach is based on a traditional machine learning method , and another approach exploits the graph partitioning algorithm and two deep neural models to generate the final clustering . To validate the efficacy of the proposed ensemble framework , it is applied on five gene expression datasets . In this paper , we propose a low-cost mapping methodology to map dynamic dataflow programs over any multi-core platform . Our approach finds interesting mapping solutions in few milliseconds that makes it doable at regular time by translating it in an equivalent graph partitioning problem . Consequently , a good load balancing over the targeted platform can be maintained even with such unpredictable applications . In this paper , we consider an optimization problem arising in the management of a new hybrid hardware and linker-assisted approach for cache memory management . A graph partitioning formulation is presented and different ILP formulations are proposed , obtained by strengthening and/or relaxing constraints and by reducing the number of integer variables . The formulations are tested on large benchmarks ( with thousands of nodes and edges ) arising from real applications . Still , since the tracking is done using radio bandwidth that is needed for telephone calls , it is important to carefully design the service areas to minimize the mobility management load . The authors define the characteristics that are needed in the service areas for digital cellular systems and state the service area design problem as a graph partitioning optimization problem where the goal is to minimize the diameter of the resulting subgraphs . A heuristic technique is then proposed to design service areas in a systematic and efficient way . The most important issue of big data processing is the relevance of analytical data ; thought of this paper is to analyze the data as a graph optimal partitioning problem . Computing all circuit graphics firstly , calculated frequent map and redrawing of the system structure according to the results , the core problem is the time complexity of the algorithm . To achieve this , we introduce a new abstraction called Silo , which is key to modeling Pixida 's scheduling goals as a graph partitioning problem . Furthermore , we show that existing graph partitioning problem formulations do not map to how big data jobs work , causing their solutions to miss opportunities for avoiding data movement . To address this , we formulate a new graph partitioning problem and propose a novel algorithm to solve it . Then in Chapters 3 and 4 , we study two extremal problems using the probabilistic method , where the statement of the problem seemingly has nothing to do with probability . The first problem is a partitioning problem of graphs , and second is a problem of measuring self similarity of a graph . In Chapters 5 and 6 , we study problems that lie in the intersection of extremal and probabilistic combinatorics ; we take a classical theorem proved by Dirac , and further study it from various view points . The power grid data management based on graph data has gained more and more attention , and its efficient storage and processing have become a research hotspot . In this paper , a subgraph partitioning algorithm for power grid graph data is developed . The power grid graph data are partitioned into multiple subgraphs , and then a parallel topology analysis algorithm for multiple subgraphs is proposed . We then incorporate these part - to - person association responses into a framework for jointly reasoning about assignment of body joints within the image and over time . To that end we use the graph partitioning formulation that has been used for people tracking and pose estimation in the past , but has not been shown to enable articulated people tracking . To facilitate efficient inference in video we resort to fast inference methods based on local combinatorial optimization and aim for a sparse model that keeps the number of connections between variables to a minimum . Performing inference ( TD / BU + SP ) improves the performance to % AP , due to more optimal assignment of part detection candidates to corresponding persons . Graph simplification in TD / BU allows to further reduce the inference time for graph partitioning ( vs. for BU - sparse ) . Single Frame Tracking Single Frame Tracking Comparison to the State of the Art . Our approach defines a model that jointly groups body part proposals within each video frame and across time . Grouping is formulated as a graph partitioning problem that lends itself to efficient inference with recent local search techniques . Our approach improves over state - of - the - art while being substantially faster compared to other related work . In particular , GPN designs a generative model that performs one feed - forward pass to efficiently generate robust person detections with joint partitions , relying on dense regressions from global joint candidates in an embedding space parameterized by centroids of persons . In addition , GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem , and conducts local optimization for each person detection with reliable global affinity cues , leading to complexity reduction and performance improvement . GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor . Link partitioning is discussed in the context of packet-level simulations of telecommunication networks . The parallelism of link partitioning relative to node partitioning is examined in terms of an idealized execution using the well-known YAWNS synchronization algorithm . Further , a critical path analysis suggests that there is much more parallelism available in these simulations than can be exploited using the YAWNS algorithm . Specifically , we suggest a node partition method for constructing an efficient OvA-binary tree , genetic algorithm for generating patterns for a node under consideration , and OvA-binary tree exploration method for performing multi-class classification . Through a numerical experiment using benchmark datasets from the UCI machine-learning repository , we confirm that ( i ) the suggested node partition method is efficient compared to a random partition method , and ( ii ) the classification performance of HMC-LAD is superior to existing multi-class LAD algorithms and other supervised learning approaches . The proposed HMC-LAD can be applied to expert and intelligent systems to effectively categorize large amount of data in knowledge base and perform inference for decision making . In contrast , detecting change in larger scale structure of the network is more challenging and not as well understood . We address this problem by proposing a framework for detecting change in network structure based on separate pieces : a probabilistic model for partitioning nodes by their behavior , a label-unswitching heuristic , and an approach to change detection for sequences of complex objects . We examine the performance of one instantiation of such a framework using mostly previously available pieces . However , the heavy computation requirements and message overhead for collecting geographical location data keep centralized algorithms out of most distributed scenarios . In this article , Based on the idea of coverage compensation a distributed node partition algorithm for random deployments is presented to generate a minimum cover set by using the optimal node distributions created by the centralized algorithms such as GA . A Genetic Algorithm for coverage is proposed too to demonstrate how an optimal coverage node distribution created by GA can be used in a distributed scenario . Because overlapping community is one of the characteristics of real‐world networks and should be considered for community detection , in this article , we propose an algorithm , called link‐based label propagation algorithm ( LinkLPA ) , to detect overlapping communities . Because the link partition is conceptually natural for the problem of overlapping community detection , LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm . Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over‐overlapping and incorrect partition of weak ties . We illustrate its relevance on real-world examples ( mobile phone and Twitter reply-to networks ) . Our main message is that any node-partitioning analysis performed on aggregated networks should be interpreted with caution , as the outcome may be strongly influenced by the level of the aggregation . The group of classical graph-theoretic problems , including graph colouring , clique cover , and maximal clique , may be viewed as instances of a general node partitioning problem ( NPP ) . A wide variety of real life problems can be modelled as instances of NPP . The performance improvement afforded by parallel processing of large-node B-trees is investigated . A node partitioning scheme to enhance concurrency for B-trees is presented . In the method , the tree height is reduced by increasing the node size . We write to denote the distance between and , that is , the length of the shortest path between and . is the - neighborhood of a node , that is , all nodes that are adjacent to . Labeling and Node Partitions . Patchy - san utilizes graph labelings to impose an order on nodes .
Finally all distances among linked nodes will converge into a stable pattern , and communities can be intuitively identified . Thanks to the dynamic viewpoint of community detection , Attractor has several potential attractive properties : ( a ) Attractor provides an intuitive solution to analyze the community structure of a network , and faithfully captures the natural communities ( with high quality ) . ( b ) Owing to its time complexity $ O(|E|)$ , Attractor allows finding communities on large networks .
While bipartite networks exhibit community structure like their unipartite counterparts , existing approaches to bipartite community detection have drawbacks , including implicit parameter choices , loss of information through one-mode projections , and lack of interpretability . Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model , which explicitly includes vertex type information and may be trivially extended to k-partite networks . This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results .
An efficient way to detect a community corresponding to a specific topic can greatly help users to obtain useful information . However , existing community detecting algorithms are all based on pages as units , the topic drift phenomenon becomes the inherent problem of each existing community detecting algorithm since the web pages always have multiple topics in content . This paper puts forward a new community detecting algorithm based on blocks as units , which combines the page dividing algorithm with the primary community detecting algorithm , overcoming the defect in the existing community detecting algorithms .
Such methods are only designed with links of the underlying social network . However , with the development of recent applications , rich edge content can be available to give another view to the community detection process . In this study , we focus on improving community detection with the edge content in social networks .
We present the results of a community detection analysis of the Wikipedia graph . Distinct communities in Wikipedia contain semantically closely related articles .
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 ) .
It is necessary to unveil the community structure of online participants to overcome this limitation . Previous community detection studies usually account for the structural factor of social networks to build their models . Based on the affect theory of social exchange , this research argues that emotions involved in social interactions should be considered in the community detection process .
In this paper , we study how to incorporate node diversity into influence maximization ( IM ) . We consider diversity as a reverse measure of the average similarity between selected nodes , which can be specified using node embedding or community detection results . Our goal is to identify a set of nodes which are simultaneously influential and diverse .
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 ] .
The current paper reports about the development of a cluster ensemble based technique combining the concepts of multiobjective optimization and deep-learning models for gene clustering where some additional protein-protein interaction information are utilized for generating the consensus partitioning . The first approach is based on a traditional machine learning method , and another approach exploits the graph partitioning algorithm and two deep neural models to generate the final clustering . To validate the efficacy of the proposed ensemble framework , it is applied on five gene expression datasets .
In this paper , we propose a low-cost mapping methodology to map dynamic dataflow programs over any multi-core platform . Our approach finds interesting mapping solutions in few milliseconds that makes it doable at regular time by translating it in an equivalent graph partitioning problem . Consequently , a good load balancing over the targeted platform can be maintained even with such unpredictable applications .
In this paper , we consider an optimization problem arising in the management of a new hybrid hardware and linker-assisted approach for cache memory management . A graph partitioning formulation is presented and different ILP formulations are proposed , obtained by strengthening and/or relaxing constraints and by reducing the number of integer variables . The formulations are tested on large benchmarks ( with thousands of nodes and edges ) arising from real applications .
Still , since the tracking is done using radio bandwidth that is needed for telephone calls , it is important to carefully design the service areas to minimize the mobility management load . The authors define the characteristics that are needed in the service areas for digital cellular systems and state the service area design problem as a graph partitioning optimization problem where the goal is to minimize the diameter of the resulting subgraphs . A heuristic technique is then proposed to design service areas in a systematic and efficient way .
The most important issue of big data processing is the relevance of analytical data ; thought of this paper is to analyze the data as a graph optimal partitioning problem . Computing all circuit graphics firstly , calculated frequent map and redrawing of the system structure according to the results , the core problem is the time complexity of the algorithm .
To achieve this , we introduce a new abstraction called Silo , which is key to modeling Pixida 's scheduling goals as a graph partitioning problem . Furthermore , we show that existing graph partitioning problem formulations do not map to how big data jobs work , causing their solutions to miss opportunities for avoiding data movement . To address this , we formulate a new graph partitioning problem and propose a novel algorithm to solve it .
Then in Chapters 3 and 4 , we study two extremal problems using the probabilistic method , where the statement of the problem seemingly has nothing to do with probability . The first problem is a partitioning problem of graphs , and second is a problem of measuring self similarity of a graph . In Chapters 5 and 6 , we study problems that lie in the intersection of extremal and probabilistic combinatorics ; we take a classical theorem proved by Dirac , and further study it from various view points .
The power grid data management based on graph data has gained more and more attention , and its efficient storage and processing have become a research hotspot . In this paper , a subgraph partitioning algorithm for power grid graph data is developed . The power grid graph data are partitioned into multiple subgraphs , and then a parallel topology analysis algorithm for multiple subgraphs is proposed .
We then incorporate these part - to - person association responses into a framework for jointly reasoning about assignment of body joints within the image and over time . To that end we use the graph partitioning formulation that has been used for people tracking and pose estimation in the past , but has not been shown to enable articulated people tracking . To facilitate efficient inference in video we resort to fast inference methods based on local combinatorial optimization and aim for a sparse model that keeps the number of connections between variables to a minimum .
Performing inference ( TD / BU + SP ) improves the performance to % AP , due to more optimal assignment of part detection candidates to corresponding persons . Graph simplification in TD / BU allows to further reduce the inference time for graph partitioning ( vs. for BU - sparse ) . Single Frame Tracking Single Frame Tracking Comparison to the State of the Art .
Our approach defines a model that jointly groups body part proposals within each video frame and across time . Grouping is formulated as a graph partitioning problem that lends itself to efficient inference with recent local search techniques . Our approach improves over state - of - the - art while being substantially faster compared to other related work .
In particular , GPN designs a generative model that performs one feed - forward pass to efficiently generate robust person detections with joint partitions , relying on dense regressions from global joint candidates in an embedding space parameterized by centroids of persons . In addition , GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem , and conducts local optimization for each person detection with reliable global affinity cues , leading to complexity reduction and performance improvement . GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor .
Link partitioning is discussed in the context of packet-level simulations of telecommunication networks . The parallelism of link partitioning relative to node partitioning is examined in terms of an idealized execution using the well-known YAWNS synchronization algorithm . Further , a critical path analysis suggests that there is much more parallelism available in these simulations than can be exploited using the YAWNS algorithm .
Specifically , we suggest a node partition method for constructing an efficient OvA-binary tree , genetic algorithm for generating patterns for a node under consideration , and OvA-binary tree exploration method for performing multi-class classification . Through a numerical experiment using benchmark datasets from the UCI machine-learning repository , we confirm that ( i ) the suggested node partition method is efficient compared to a random partition method , and ( ii ) the classification performance of HMC-LAD is superior to existing multi-class LAD algorithms and other supervised learning approaches . The proposed HMC-LAD can be applied to expert and intelligent systems to effectively categorize large amount of data in knowledge base and perform inference for decision making .
In contrast , detecting change in larger scale structure of the network is more challenging and not as well understood . We address this problem by proposing a framework for detecting change in network structure based on separate pieces : a probabilistic model for partitioning nodes by their behavior , a label-unswitching heuristic , and an approach to change detection for sequences of complex objects . We examine the performance of one instantiation of such a framework using mostly previously available pieces .
However , the heavy computation requirements and message overhead for collecting geographical location data keep centralized algorithms out of most distributed scenarios . In this article , Based on the idea of coverage compensation a distributed node partition algorithm for random deployments is presented to generate a minimum cover set by using the optimal node distributions created by the centralized algorithms such as GA . A Genetic Algorithm for coverage is proposed too to demonstrate how an optimal coverage node distribution created by GA can be used in a distributed scenario .
Because overlapping community is one of the characteristics of real‐world networks and should be considered for community detection , in this article , we propose an algorithm , called link‐based label propagation algorithm ( LinkLPA ) , to detect overlapping communities . Because the link partition is conceptually natural for the problem of overlapping community detection , LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm . Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over‐overlapping and incorrect partition of weak ties .
We illustrate its relevance on real-world examples ( mobile phone and Twitter reply-to networks ) . Our main message is that any node-partitioning analysis performed on aggregated networks should be interpreted with caution , as the outcome may be strongly influenced by the level of the aggregation .
The group of classical graph-theoretic problems , including graph colouring , clique cover , and maximal clique , may be viewed as instances of a general node partitioning problem ( NPP ) . A wide variety of real life problems can be modelled as instances of NPP .
The performance improvement afforded by parallel processing of large-node B-trees is investigated . A node partitioning scheme to enhance concurrency for B-trees is presented . In the method , the tree height is reduced by increasing the node size .
We write to denote the distance between and , that is , the length of the shortest path between and . is the - neighborhood of a node , that is , all nodes that are adjacent to . Labeling and Node Partitions . Patchy - san utilizes graph labelings to impose an order on nodes .
Community Detection via Local Dynamic Interaction Efficiently inferring community structure in bipartite networks An Approach of Community Detecting Based on Block Level Link Analysis Overlapping community detection in large networks from a data fusion view Analysis of Community Structure in Wikipedia (Poster) Scalable community detection for social networks Exploiting Emotions in Social Interactions to Detect Online Social Communities Incorporating Diversity into Influential Node Mining DeepWalk: online learning of social representations Ensembling of Gene Clusters utilizing Deep Learning and Protein-protein Interaction Information. Towards run-time actor mapping of dynamic dataflow programs onto multi-core platforms Solving Graph Partitioning Problems Arising in Tagless Cache Management How to assign service areas in a cellular mobile telephone system A Novel Clustering Algorithm for Large-Scale Graph Processing PIXIDA: Optimizing Data Parallel Jobs in Wide-Area Data Analytics Problems in Extremal and Probabilistic Combinatorics Research on Parallel Analysis of Power Grid Graph Data ArtTrack: Articulated Multi-Person Tracking in the Wild Generative Partition Networks for Multi-Person Pose Estimation Link Partitioning in Parallel Simulation of Scale-Free Networks Hierarchical multi-class LAD based on OvA-binary tree using genetic algorithm Context-aided analysis of community evolution in networks A performance evaluation of a coverage compensation based algorithm for wireless sensor networks LinkLPA: A Link‐Based Label Propagation Algorithm for Overlapping Community Detection in Networks Measuring the effect of node aggregation on community detection Genetic Algorithm for Node Partitioning Problem and Applications in VLSI Design Parallel Processing of Large Node B-Trees Learning Convolutional Neural Networks for Graphs
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2014 2014 2009 2014 2009 2016 2010 2018 2014 2019 2013 2014 1994 2016 2015 2012 2018 2017 2017 2016 2015 2017 2008 2017 2018 1990 2016
ASONAM PACIS KDD '14 ISPA ISCO ICIC (3) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv DS-RT MSWiM ICML
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