An Efficient Keywords Search in Temporal Social Networks

Abstract With the increasing of requirements from many aspects, various queries and analyses arise focusing on social network. Time is a common necessary dimension in types networks. Social networks with time information are called temporal networks, which can be when user sends message to another user. Keywords search consists finding relationships between group users that has set query labels valid within interval. It provides assistance network analysis, classification users, community detection, etc. However, existing methods have limitations solving keyword problems. We propose basic algorithm, discrete timestamp intention turning problem into traditional also an approximative algorithm based but it still suffers algorithms’ low efficiency. To further improve performance, we new dynamic programming solve The main idea extend vertex solution by edge-growth operation tree-merger operation. two powerful pruning techniques reduce intermediate results during extension. Additionally, all algorithms proposed capable handling variety ranking functions, them made conform top-N querying. efficiency effectiveness verified through extensive empirical studies.