ℓ1-Graph Based Community Detection in Online Social Networks
نویسندگان
چکیده
Detecting community structures in online social network is a challenging job for traditional algorithms, such as spectral clustering algorithms, due to the unprecedented large scale of the network. In this paper, we present an efficient algorithm for community detection in online social network, which chooses relatively small sample matrix to alleviate the computational cost. We use -graph to construct the similarity graph and integrate the graph laplacian with random walk in directed social network. The experimental results show the effectiveness of the proposed method.
منابع مشابه
An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks
The structure of the community is one of the important features of social networks. A community is a sub graph which nodes have a lot of connections to nodes of inside the community and have very few connections to nodes of outside the community. The objective of community detection is to separate groups or communities that are linked more closely. In fact, community detection is the clustering...
متن کاملDetecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کاملOverlapping Community Detection in Social Networks Based on Stochastic Simulation
Community detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on diffe...
متن کاملProviding a Link Prediction Model based on Structural and Homophily Similarity in Social Networks
In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Most online social networks are growing and changing with new communications (new edges). Forecasting new edges in online social networks can give us a better understanding of the growth of these networ...
متن کاملMining Overlapping Communities in Real-world Networks Based on Extended Modularity Gain
Detecting communities plays a vital role in studying group level patterns of a social network and it can be helpful in developing several recommendation systems such as movie recommendation, book recommendation, friend recommendation and so on. Most of the community detection algorithms can detect disjoint communities only, but in the real time scenario, a node can be a member of more than one ...
متن کامل