A method of website optimization including collecting data for constructing user profiles; constructing the user profiles; quantifying affinities between the user profiles; constructing a user network in which the affinities are represented as links between user nodes; constructing an adjacency matrix; calculating a first principal eigenvector of the adjacency matrix; defining a new network by removing a random link in the network and calculating a new adjacency matrix for the new network; calculating a second principal eigenvector of the new adjacency matrix; calculating a vector of relative shifts between the first principal eigenvector and the second principal eigenvector; for every node, assigning a value for a direction of its shift between the network and the new network; and repeating certain of the steps.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of website optimisation for a website hosted on a server system, the website for use by a plurality of users, the method including steps executed by the server system of: (i) collecting data for constructing a plurality of user profiles corresponding to the plurality of users; (ii) constructing the plurality of user profiles using the collected data; (iii) quantifying affinities between the plurality of user profiles; (iv) constructing a user network, in which users are represented as nodes, and in which the affinities are represented as links between the nodes; (v) constructing an adjacency matrix which describes the network; (vi) calculating a first principal eigenvector of the adjacency matrix; (vii) defining a new network by removing a random link in the network and calculating a new adjacency matrix for the new network; (viii) calculating a second principal eigenvector of the new adjacency matrix; (ix) calculating a vector of relative shifts between the first principal eigenvector and the second principal eigenvector; (x) for every node, assigning a value for a direction of its shift between the network and the new network; (xi) repeating steps (vii) to (x) for a plurality of links; (xii) calculating a node affinity matrix; (xiii) assigning nodes to different communities using values of elements of the node affinity matrix, to segment the user network into a plurality of communities of users, and (xiv) providing website content to each user according to the segmented community of each user. 2. The method of claim 1, wherein the first principal eigenvector and the second principal eigenvector are normalized. 3. The method of claim 1, wherein the direction of shift in step (x) is assigned using a value of ±1. 4. The method of claim 3, wherein the direction of shift in step (x) is assigned using a value of ±1 via the quantity θj(k) of Eq. (4). 5. The method of claim 1, wherein in step (xi), repetition is performed always choosing a new link, or wherein in step (xi), repetition is performed until all links have been chosen at least once, or wherein in step (xi), repetition is performed for a predefined fraction of the number of links. 6-7. (canceled) 8. The method of claim 1, the method further comprising the step of analyzing a frequency with which nodes appear in the respective communities. 9. The method of claim 1, wherein at least one threshold is used to segment the communities in step (xiii). 10. (canceled) 11. The method of claim 1, wherein members of the same community tend to behave collectively in response to structural instabilities. 12. The method of claim 1, wherein elements of the adjacency matrix are weighted. 13. (canceled) 14. The method of any-previous claim 1, wherein elements of the adjacency matrix are directed. 15-16. (canceled) 17. The method of claim 1, wherein step (xii) is performed using Eq. (6). 18. The method of claim 1, wherein collecting data is performed by collecting clickstream data. 19. The method of claim 1, wherein collected data includes web analytics data or visitor behavior data. 20. The method of claim 1, wherein collected data includes online customer data, or wherein collected data includes offline customer data. 21. (canceled) 22. The method of claim 1, the method further comprising the step of feeding collected data into a structured database schema. 23. The method of claim 1, wherein user profiles are constructed using interactions between the users and a plurality of items. 24. The method of claim 23, wherein user profiles are constructed using a user ID for each user, and a user vector U associated with each user ID, the vector U having N dimensions, where each dimension corresponds to a feature from the user and item spaces. 25. The method of claim 23, wherein affinities are quantified using a user-item matrix, wherein the matrix includes data relating to user interactions with the plurality of items. 26-27. (canceled) 28. The method of claim 1, wherein the affinities of step (iii) are quantified using a cosine similarity formula, a Pearson correlation formula, or a Jackard similarity formula. 29. The method of claim 1, wherein a thickness of a respective link between nodes corresponds to a respective affinity between user profiles corresponding to the nodes. 30. The method of claim 1, wherein segmenting the user network map into communities of users is self-adapting. 31. The method of claim 1, wherein segmenting the user network map into a plurality of communities of users includes segmenting the user network map into a number of communities, wherein the number is determined using the network map. 32. The method of claim 1, wherein segmenting the user network map into a plurality of communities of users is such that Customer Communities, cliques and hubs; authorities and peripheries; bridges and influential carriers of information are identified. 33. The method of claim 1, wherein the method includes a step of characterizing communities of users via their most prevalent attributes. 34. The method of claim 1, wherein the method is applied iteratively. 35-37. (canceled) 38. A computer implemented website optimisation system for website optimisation of a website hosted on a server system, the website for use by a plurality of users, the computer implemented website optimisation system including the server system, the server system running computer software configured to: (i) collect data for constructing a plurality of user profiles corresponding to the plurality of users; (ii) construct the plurality of user profiles using the collected data; (iii) quantify affinities between the plurality of user profiles; (iv) construct a user network, in which users are represented as nodes, and in which the affinities are represented as links between the nodes; (v) construct an adjacency matrix which describes the network; (vi) calculate a first principal eigenvector of the adjacency matrix; (vii) define a new network by removing a random link in the network and calculate a new adjacency matrix for the new network; (viii) calculate a second principal eigenvector of the new adjacency matrix; (ix) calculate a vector of relative shifts between the first principal eigenvector and the second principal eigenvector; (x) for every node, assign a value for a direction of its shift between the network and the new network; (xi) repeat (vii) to (x) for a plurality of links; (xii) calculate a node affinity matrix; (xiii) assign nodes to different communities using values of elements of the node affinity matrix, to segment the user network into a plurality of communities of users, and (xiv) provide website content to each user according to the segmented community of each user. 39. (canceled) 40. A server system hosting a website for use by a plurality of users, the server system running computer software configured to: (i) collect data for constructing a plurality of user profiles corresponding to the plurality of users; (ii) construct the plurality of user profiles using the collected data; (iii) quantify affinities between the plurality of user profiles; (iv) construct a user network, in which users are represented as nodes, and in which the affinities are represented as links between the nodes; (v) construct an adjacency matrix which describes the network; (vi) calculate a first principal eigenvector of the adjacency matrix; (vii) define a new network by removing a random link in the network and calculate a new adjacency matrix for the new network; (viii) calculate a second principal eigenvector of the new adjacency matrix; (ix) calculate a vector of relative shifts between the first principal eigenvector and the second principal eigenvector; (x) for every node, assign a value for the direction of its shift between the network and the new network; (xi) repeat (vii) to (x) for a plurality of links; (xii) calculate a node affinity matrix; (xiii) assign nodes to different communities using values of elements of the node affinity matrix, to segment the user network into a plurality of communities of users, and (xiv) provide website content to each user according to the segmented community of each user. 41-62. (canceled)