Community Overlapping Detection in Complex Networks

Transcription

1 Indian Journal of Science and Technology, Vol 9(28), DOI: /ijst/2016/v9i28/98394, July 2016 ISSN (Print) : ISSN (Online) : Community Overlapping Detection in Complex Networks Ravishanker, Ashish Kr Luhach* and Richa Sharma Lovely Professional University, Phagwara , Punjab, India; ravishanker20@gmailcom, ashishluhach@acmorg, rsricha177@gmailcom Abstract Background/Objectives: The community overlapping is the process by which number of nodes within the mesh network share common resources The shared resources could lead to the conflict such as inconsistent analysis problem Study of these problems is the objective of the paper Methods/Statistical Analysis: In order to analyze the problem Modified K-Clique with sink node elimination technique is suggested K-Clique method used detects the nodes in the mesh network having more than one connection The modification to K-Clique enhance speed since sink node is eliminated prior to calculation of cliques The adjacency matrix is used in order to detect the sink nodes The Simulation is conducted in MATLAB The MATLAB provides tools of network programming in terms of plots and graphs The existing K-Clique is compared against the modified K-Clique and result obtained is better for Modified k-clique Findings: The speed is enhanced almost by 5% and number of cliques of distinct sizes discovered is also increased by 5% The speed can further be enhanced by following hop count mechanism to reach destination quickly in addition to sink node elimination Application/Improvement: Enhancement of performance using community overlapping detection in wireless mesh network through which it is possible to transfer the data towards multiple destinations with the help of community overlapping detection Multiple destination towards which is to be transferred can be detected Time will be less consumed in this case The distance vector routing can be merged in the supposed system to further enhance the scope of the system Keywords: Community Overlapping, Complex Network, Distance Vector, K-Clique, Sink Nodes 1 Introduction Community overlapping or integrated configuration is one of the utmost widely used in real world social media as it will elaborate functioning of the system The dataset will be required in order to detect the overlapping The dataset is fetched from the Snap University The data which is fetched is converted into the adjacency matrix The adjacency matrix will indicate that nodes are adjacent or not The nodes are said to be adjacent if they are connected by the single edge If nodes are adjacent then there will be 1 in the corresponding adjacency matrix otherwise it will contain 0 The overlapping community detection will be represented through Figure 1 The overlapping community detection will help now identifying the interest of the community then presenting them the content which they most often like Social networking sites are chosen for this purpose Network societies signify basic structure for considerate the society of the real sphere environment A society will be a group of nodes which are attached by some logical links In a social network it is easier that persons in a community network are certainly characterized by several communal relationships A person can have link towards many active area including people, movies, news, etc All of the above stated active things are group to which a user may belong That s why, to identify a fixed groups interest is increasing in overlapping community detection algorithm which are not disconnect In the proposed paper comparison of the various techniques used for detection of the overlapping will be made *Author for correspondence

2 Community Overlapping Detection in Complex Networks Figure 1 2 Preliminaries The overlapping community structure The basic definitions which are used throughout this paper are presented 1 Consider a graph G = {E,V} where E is the ends and V is the vertexes For solid graphs m = O(n 2 ) and for the sparse network m = O(n) 2 The adjacency matrix will be used in order to determine which nodes are reachable from the current nodes If the elements of the adjacency matrix 1 then it means the node is reachable from the current node otherwise adjacency matrix will contains 0 In overlapping commuity 1 detection, a fixed clusters are initiated is termed as covers which are denoted by C = {c1,c2,c3, cn) In 3 the presented clusters a node can belong to multiple cluster Every vertex i associate with communal allowing to a related element The association between the nodes and cluster will be indicated with the aic This aic is a unit of asset of relationship among vertex (i) and cluster (c) There is following constraint to be satisfied in order for the node to be member of the cluster 0<=aic<=1 i V, c C Destruction started in determining every groups of range k within association When cliques are formed, an innovative grid is assembled and every node represents those shares k-1 members Connected graph will be utilized to detect the overlapping community 1 The relatively lesser values of k invention in between 3 to 6 appears to provide good results A threshold value will be maintained in this case which will be used to determine the clique determination Only individual k cliques by frequency greater than a permanent threshold are comprised into the unrestricted networks 1 CPM introduces a sub graph frequency threshold for biased graph links Only individual k cliques by frequency greater than stable values of threshold are included into unrestricted networks Rather than preceding each standard like k, Session Control Protocol seeks clique societies to a set of size For the beginning segment, Session Control Protocol identifies k groups with defining each k-2 groups towards adjacent members of binary end points after association is added into complex arrangement serially inside the arrangement reducing loads For the next stage, k societies determined through seeking associated modules inside the k 1 groups Estimate the bipartisan presentation, therefore some vertex signifies k communities then another signifies a k 1 communities Meanwhile every k community is searched just double, these successive stages raises nearly the same as the role of legion of groups It permits numerous load thresholds in an individual route and it is quicker as compared to CPM In spite of their logical simplification, one claim is that CPM-type algorithms are closer like to anomaly matching slightly seeking community to invent specific, local configuration in a complex network (Figure 2, 3) The result for k = 4 will be describe as 3 Related Work The existing work concentrates in detecting the overlapping nodes by the involvement of sink nodes In order to analyze the overlapping community detection we will use the following algorithms 31 Cliques Destruction Algorithm Cliques Destruction Algorithm is formed information that community is created with the overlapping community sets of completely connected sub graphs and distinguishes societies by seeking for end-to-end cliques Cliques Figure 2 The overlapping community of order 4 2 Vol 9 (28) July 2016 wwwindjstorg Indian Journal of Science and Technology

3 Ravishanker, Ashish Kr Luhach and Richa Sharma Figure 4 Uncertain clustering mechanism Figure 3 k-clique mechanisms 32 Overlapping Community Detection by Uncertain Technique Uncertain community model is used to determine the strength of the groupies that exists in between nodes and edges For 1 overlapping community detection generally c means clustering is preferred but it is static in nature A soft membership function will be determined for this purpose Algorithms to identify overlying societies can be crispy or Uncertainty through intention: It give crispy or uncertain panels irrespective type of overlying societies in the complex network Towards associate such algorithms together reliably, we recommend a mutual size: The uncertain rules Rand Index To determine a uncertain algorithm, we link the uncertain division method to build the complex network that formed by the algorithm To determine a crispy algorithm, we first transform the division seek through algorithm to an uncertain method by accumulating the identical fitting constants for both community Then the vertexes v related to K societies in the crispy division, it related to coefficient is 1/K in those overlapping societies and 0 in further societies, in the uncertain division 1 Some would believe this hypothetical uncertain division toward remain inferior to any seeked in a respectable uncertain algorithm, as it covers no information around the attached constants To determine an uncertain procedure on a crispy networks, we change the crispy division algorithm by using the uncertain method in the similar method to the network, and comparison will be performed through uncertain division originate by the procedure In case both algorithm and network remain similar, we translate both divisions (inventive and sleeked by the algorithm) to uncertain method and equate them by the uncertain Rand Index In last, we give a modest method to attaining a non-trivial uncertain division after a single crispy Every existence of node i in communities (c), we give a related constant αic Those equals to the legions of i s neighbors which arise in c distributed by the range of c, regularized through regular way The mechanism, those we call Make uncertainty, can be utilized to change any crispy procedure to a single uncertainty, it may give good result than the crispy procedure, we check this supposition through our tests (Figure 4) 33 Line Graph and Link Partitioning This technique will follow the partitioning of a link rather than nodes to analyze the cluster 2 A node is said to be overlapped if the link connected to it is a part of many clusters Links are generally clusters using the hierarchical clustering techniques Single linkage hierarchical clustering is then represented by the dendogram If these dendograms are cut at certain threshold value then overlapping communities are generated 1 11 The edges are analyzed in this case The edges belonging to more than Vol 9 (28) July 2016 wwwindjstorg Indian Journal of Science and Technology 3

4 Community Overlapping Detection in Complex Networks 4 Proposed Work Figure 5 The dendrogram describing overlapping community detection one region define community overlapping Line graph is also extended to clique graph Link partitioning for overlapping community is also proposed Link partitioning will help in identifying overlapping community Higher quality detection is provided in this case The node based detection is not used in this case The concepts define within the line graph and link partitioning is ambiguous Link based extended modularity is also purposed in this case The modularity will decrease the complexity associated with the system Link between the nodes i and j 1 (Figure 5) 34 Problem Definition As in existing system to detect community overlapping we were considering the sink nodes But in our proposed system we haven t consider the sink nodes to detect community overlapping because it consuming so much time So 2 4,7,10 to detect overlapping we used K-Clique algorithm The 12 CPM is built on the fact of created a community with the overlapping community sets of completely connected sub graphs and identify community by seeking end-to-end cliques In the existing system it is very difficult to specify the cluster group manually because it can vary every time when the group formulate To get rid of this problem instead of considering sink node we consider the adjacent node with value 1 In the whole network the adjacent nodes with value 1 will be considered and value 0 will be discarded The proposed algorithm will takes the adjacency matrix from the graph and then eliminate the nodes having 0 in the corresponding row matrix Thus time will be saved In this case, the k-clique algorithm will be modified The proposed algorithm will be described as follows: Part I For i = 1, 2,, n in turn Initialize the clique Q i = {i} Perform procedure on Q i For r = 1, 2,, k perform If adj [r] > 0 then Q i + 1 End of if End of for The result is a maximal clique Q i Part II For each pair of maximal cliques Q i, Q j found in Part I Initialize the clique Q i, j Q j Perform procedure on Q i, j For r = 1, 2,, k perform procedure repeated r times Remove Redundancy using Q i, j Q j The result is a maximal clique Q i, j Here clique will indicate number of community to which a given object belongs to The proposed algorithm will produce better result as compared to other existing algorithm 5 Results The proposed algorithm produces better result as compared to other algorithm this will be described by the use of simulation and comparison tables The modularity is also calculated in this case The modularity will described in terms of the nodes in which nodes are further divided The number of cliques will going to decide the complexity of the system within the network The community overlapping detection will be inspired by the fact that the modules present within the system interact with each other The interaction can either be high or low The complexity of the system can be decreased if the modules which are used are high The numbers of modules which are used within the given system will going to decide the modularity In other words modularity of the system should be high In the existing system modularity is given as 4 Vol 9 (28) July 2016 wwwindjstorg Indian Journal of Science and Technology

5 Ravishanker, Ashish Kr Luhach and Richa Sharma is useful in detecting the cliques The K-Clique method is one of the simplest methods for the detection of the overlapping community detection This method is modified in the proposed work by eliminating sink nodes 7 References Figure 6 K-Means Hierarchal K-Clique Modified The comparison of various techniques in terms of the result will be as follows Table 1 The performance of Modified Clique algorithm in terms of time is also better The K-clique algorithm without considering sink nodes is as follows Figure 6 From the above result it is clear that the Modified K-Clique without Sink nodes is better in every aspect 6 Conclusion Time comparison of algorithm Table 1 Describing the results of various algorithms regarding community overlapping K-Clique(Modified) Fuzzy Detection Hierarchal Clustering Number of Nodes Clique Size Nodes Compared Cliques Time Consumed 10ms 21ms 26ms The study of the Modified k-clique algorithm suggests that the result will improve by eliminating the sink nodes from the graph The adjacency matrix where value greater than 1 is placed is considered The comparison table also indicates that the fuzzy method will give least results as the sink nodes are considered Detection mechanisms are many We highlighted the methods and also described which method 1 Lu Z, Sun X, Wen Y, Cao G, La Porta T Algorithms and applications for community detection in weighted networks IEEE Transactions on Parallel and Distributed Systems 2015 Nov 1; 26(11): Shang J, Liu L, Li X, Xie F, Wu C Epidemic spreading on complex networks with overlapping and non-overlapping community structure Physica A: Statistical Mechanics and its Applications 2015 Feb 1; 419: Bandyopadhyay S, Chowdhary G, Sengupta D FOCS: Fast Overlapped Community Search IEEE Transactions on Knowledge and Data Engineering 2015 Nov 1; 27(11): Zhang Q, Qirong Q, Guo K Parallel overlapping community discovery based on grey relational analysis 2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS); 2015 Aug 18 p Chen D, Fu Y, Shang M An efficient algorithm for overlapping community detection in complex networks IEEE WRI Global Congress on Intelligent Systems GCIS 09; 2009 May 19 p Xie J, Kelley S, Szymanski BK Overlapping community detection in networks: The state-of-the-art and comparative study ACM Computing Surveys (CSUR) 2013 Aug 1; 45(4):43 7 Crampes M, Plantie M Overlapping community detection optimization and nash equilibrium ACM Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics; 2015 Jul 13 p 2 8 Whang J, Gleich D, Dhillon I Overlapping community detection using neighborhood-inflated seed expansion IEEE Trans Knowl Data Eng 2016 May; 28(5): Meena J, Devi VS Overlapping community detection in social network using disjoint community detection 2015 IEEE Symposium Series on Computational intelligence; 2015 Dec 7 p He K, Sun Y, Bindel D, Hopcroft JE, Li Y Detecting overlapping communities from local spectral subspaces arxiv preprint arxiv: Sep Coscia M, Rossetti G, Giannotti F, Pedreschi D Uncovering hierarchical and overlapping communities with a local-first approach ACM Transactions on Knowledge Discovery from Data (TKDD) 2014 Oct 28; 9(1):6 12 Angadi A, Varma PS Overlapping community detection in temporal networks Indian Journal of Science and Technology 2015 Dec, 8(31):1 6 Vol 9 (28) July 2016 wwwindjstorg Indian Journal of Science and Technology 5