Influential Attributed Communities Search in Large Networks (InfACom)

Community search is a fundamental problem in graph analysis. In many applications, network nodes have specific properties that are essential for making sense of communities. In these networks, attributes are associated with nodes to capture their properties. The community influence is a key property of the community that can be employed to sort the communities in a network based on the relevance/importance of certain attributes. Unfortunately, most of the previously introduced community search algorithms over attributed networks neglected the community influence. In this paper, we study the influential attributed community search problem. Different factors for measuring the influence are discussed. Also, different Influential Attributed Community (InfACom) algorithms based on the concept of k-clique are proposed. Two techniques are presented one for sequential implementation with three variations and one for parallel implementation. In addition, we propose efficient algorithms for maintaining the proposed algorithms on dynamic graphs. The proposed algorithms are evaluated on different real datasets. The experimental results show that the summarization technique reduces the size of the graph by approximately half. In addition, it shows that the proposed algorithms
$EnhancedExact$
and
$Approximate$
outperform the state-of-the-art approaches Incremental Time efficient
$(Inc-T)$
, Incremental Space efficient
$(Inc-S)$
,
$Exact$
, and 2-Approximation
$(AppInc)$
in both efficiency and effectiveness. For the
$EnhancedExact$
algorithm, the results show that the efficiency is at least 7 times faster than the
$Inc-S$
algorithm, at least 4.5 times faster than the
$Inc-T$
algorithm, and 2 times faster than the
$AppInc$
algorithm. For the
$Approximate$
algorithm, the results show that its efficiency is at least 10 times faster than the
$Inc-S$
algorithm, at least 6.4 times faster than the
$Inc-T$
algorithm, and 3 times faster than the
$AppInc$
algorithm. Finally, the results show that the proposed algorithms retrieve cohesive communities with a smaller diameter than all the state-of-the-art approaches.