NOWADAYS, THE EMERGENCE OF ONLINE SOCIAL NETWORKS HAVE EMPOWERED PEOPLE TO EASILY SHARE INFORMATION AND MEDIA WITH FRIENDS. INTERACTING USERS OF SOCIAL NETWORKS WITH SIMILAR USERS AND THEIR FRIENDS FORM Community STRUCTURES OF NETWORKS. UNCOVERING COMMUNITIES OF THE ONLINE USERS IN SOCIAL NETWORKS PLAYS AN IMPORTANT ROLE IN NETWORK ANALYSIS WITH MANY APPLICATIONS SUCH AS FINDING A SET OF EXPERT USERS, FINDING A SET OF USERS WITH COMMON ACTIVITIES, FINDING A SET OF SIMILAR PEOPLE FOR MARKETING GOALS, TO MENTION A FEW. ALTHOUGH, SEVERAL ALGORITHMS FOR DISJOINT Community Detection HAVE BEEN PRESENTED IN THE LITERATURE, ONLINE USERS SIMULTANEOUSLY INTERACT WITH THEIR FRIENDS HAVING DIFFERENT INTERESTS. ALSO USERS ARE ABLE TO JOIN MORE THAN ONE GROUP AT THE SAME TIME WHICH LEADS TO THE FORMATION OF Overlapping COMMUNITIES. THUS, FINDING Overlapping COMMUNITIES CAN REALIZE A REALISTIC ANALYSIS OF NETWORKS. IN THIS PAPER, WE PROPOSE A FAST ALGORITHM FOR Overlapping Community Detection. IN THE PROPOSED ALGORITHM, IN THE FIRST PHASE, THE LOUVAIN METHOD IS APPLIED TO THE GIVEN NETWORK AND IN THE SECOND PHASE A BELONGING MATRIX IS UPDATED WHERE AN EACH ELEMENT OF BELONGING MATRIX DETERMINES HOW MUCH A NODE BELONGS TO A Community. FINALLY, SOME OF THE FOUND COMMUNITIES ARE MERGED BASED ON THE MODULARITY MEASURE. THE PERFORMANCE OF THE PROPOSED ALGORITHM IS STUDIED THROUGH THE SIMULATION ON THE POPULAR NETWORKS WHICH INDICATES THAT THE PROPOSED ALGORITHM OUTPERFORMS SEVERAL WELL-KNOWN Overlapping Community Detection ALGORITHMS.

Community Detection IN SOCIAL NETWORKS IS USUALLY DONE BASED ON THE DENSITY OF CONNECTIONS BETWEEN GROUPS OF NODES. HOWEVER, THESE LINKS DO NOT NECESSARILY REPRESENT AN ACTUAL FRIENDSHIP ESPECIALLY IN ONLINE SOCIAL NETWORKS. THERE ARE USERS WITH DECLARED FRIENDSHIP CONNECTIONS BUT WITHOUT ACTUAL COMMUNICATION AND NO COMMON INTERESTS. MOST OF THE WORKS IN THIS AREA CAN BE DIVIDED INTO TWO GROUPS: TOPOLOGY-BASED AND TOPIC-BASED. THE FORMER USUALLY LEADS TO COMMUNITIES EACH CONTAINING DIVERSE TOPICS, AND THE LATTER LEADS TO COMMUNITIES EACH WITH A CONSISTENT TOPIC BUT WITH DIVERSE STRUCTURE. IN THIS PAPER, WE MEASURE THE SIMILARITY BETWEEN USERS USING TOPIC MODELS TO GENERATE VIRTUAL LINKS FOR USERS WITH COMMON INTERESTS. MOREOVER, IN ORDER TO REDUCE THE EFFECT OF USELESS LINKS BETWEEN USERS, WE WEIGHT THE NETWORK BY MEASURING SIMILARITY OF USERS’ TOPICS, SO WE COULD GENERATE CONFORMING COMMUNITIES, WHICH CONTAIN ONLY ONE TOPIC OR A GROUP OF CONSISTENT TOPICS. THE TEST RESULTS ON ENRON EMAIL DATASET HAVE SHOWN THE SUPERIOR PERFORMANCE OF OUR PROPOSED METHOD IN THE TASK OF Community Detection. ...

Social network analysis with large volumes of data and complex communication structures is so difficult and time-consuming. Community Detection is one of the major challenges in network analysis. A Community is a set of individuals or organizations whose communication density is more than other network entities. Community Detection or clustering can reveal the structure of groups in social networks, or relationships between entities. The label propagation algorithms with neighbor node influence have less complexity than traditional algorithms, such as clustering, to recognize communities. Also, the algorithms can identify Overlapping communities. In our label propagation algorithm, which is based on the neighbor node influence, important nodes are more likely to publish their labels, while less important nodes have a small chance of spreading the label. The degree of similarity of nodes and the effect of nodes in a social network depends on the parameter of path length between nodes. In the proposed method, increasing this parameter leads to more accurate identification of Overlapping and stable communities. The proposed algorithm detects Overlapping communities with the same accuracy as the previous algorithms with fewer iterations, in less time. The algorithm is implemented on real and artificial social networks with weightless graphs and weighted graphs with weighting by Jacquard similarity criterion, in all of which the execution time is improved.

Community Detection IS ONE OF THE MOST IMPORTANT TASKS IN SOCIAL NETWORKS ANALYSIS. THIS PROBLEM BECOMES MORE CHALLENGING WHEN THE STRUCTURE OF THE NETWORK CHANGES DURING THE TIME. IT IS VERY IMPORTANT TO UPDATE THE STRUCTURES OF THE Community IN A DYNAMIC NETWORK WITHOUT TIME-CONSUMING PROCEDURES. THIS PAPER SUGGESTS A HYBRID EVOLUTIONARY ALGORITHM FOR ONLINE Community Detection. THE PROPOSED ALGORITHM CALLED MEMETIC BASED ONLINE Community Detection (MBOC) IS BASED ON A MEMETIC ALGORITHM WITH NEW GENETIC OPERATORS AND A NOVEL STOCHASTIC LOCAL SEARCH TO ASSIGN NEW NODES TO COMMUNITIES AND ANOTHER LOCAL SEARCH CALLED DENSE SEARCH TO MODIFY COMMUNITIES AFTER NEW ASSIGNMENTS. THE METHOD IS EVALUATED OVER SEVERAL WELL-KNOWN BENCHMARK NETWORKS. THE RESULTS SHOW THAT THE PROPOSED APPROACH OUTPERFORMS THE PREVIOUS METHODS IN MOST CASES.