Abstract
Community search is the task of discovering dense subgraph that satisfy a set of given query parameters. Most community search algorithms consider link structure while ignoring link weight. A recent study proposed the idea of discovering weighted communities which focuses on both link structure and link weight using an online search approach and index-based approach. In this paper two online algorithms are proposed to scale-up the existing online approach efficiency. Performance evaluation of the proposed algorithms against the existing online approach over different datasets shows a great improvement in terms of search and query evaluation time.
Highlights
Due to the rapid growth of complex data, the graph model has been increasingly used to model various interactions between entities
Community structure exists as a subgraph of strongly interconnected vertices [1]
Community search is the task of discovering communities that contain a given set of query nodes or satisfy certain query parameters
Summary
Due to the rapid growth of complex data, the graph model has been increasingly used to model various interactions between entities. There are many interesting applications such as social networks, biological networks, and citation networks that have been modeled as a graph. In such applications, community structure exists as a subgraph of strongly interconnected vertices [1]. One of the main task is to discover such community structure using community search approaches. Community search is the task of discovering communities that contain a given set of query nodes or satisfy certain query parameters. The majority of those studies ignore other community aspects and mainly the edge weight. Ignoring the edge weight causes the loss of important information within the discovered communities. Are some examples of real networks where edge weight has a significant role:
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