2 research outputs found
Continuous Influence-based Community Partition for Social Networks
Community partition is of great importance in social networks because of the
rapid increasing network scale, data and applications. We consider the
community partition problem under LT model in social networks, which is a
combinatorial optimization problem that divides the social network to disjoint
communities. Our goal is to maximize the sum of influence propagation
through maximizing it within each community. As the influence propagation
function of community partition problem is supermodular under LT model, we use
the method of Lov{}sz Extension to relax the target influence
function and transfer our goal to maximize the relaxed function over a matroid
polytope. Next, we propose a continuous greedy algorithm using the properties
of the relaxed function to solve our problem, which needs to be discretized in
concrete implementation. Then, random rounding technique is used to convert the
fractional solution to integer solution. We present a theoretical analysis with
approximation ratio for the proposed algorithms. Extensive experiments
are conducted to evaluate the performance of the proposed continuous greedy
algorithms on real-world online social networks datasets and the results
demonstrate that continuous community partition method can improve influence
spread and accuracy of the community partition effectively.Comment: arXiv admin note: text overlap with arXiv:2003.1043