Information Dessimination Main Path Detection in Social Network Based on Communities Structure

Identifying the main path of disseminating information on online social networks is a difficult task but important. Finding a path between the two nodes that crosses higher-impact nodes will help to accurately analyze the social network, release information faster, and even prevents them from spreading. In large-scale social networks, traditional algorithms such as Dijkstra do not have to be effective because of the high complexity of time. To solve this problem, we can use social networking features such as groups or communities. Similar to what is to solve the traffic problem on the network, with the discovery of communities, it can create a hierarchical structure to reduce the search space dramatically. In the present study, the communities are based on the assumption that each node is a member of a community most of its neighbors, and each node is identified by its community with its neighbors. The algorithm has been tested on three datasets: Karate, Net Science, and YouTube. The test results indicate that the search time is improved in finding the main path or the shortest path based on crossing the high-impact nodes between the two nodes.

Keywords:

Social network , shortest path , dijkstra , information path , community detection