Parameter-Free Structural Diversity Search

Abstract

The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of discriminative core, which automatically models various kinds of social contexts without parameter t. Leveraging on discriminative cores and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameterfree structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods. ACM Reference Format:

Top-k Graph Summarization on Hierarchical DAGs

“…Top-k diversification. There exist several studies on the top-k graph diversification [5,10,12,13,21,22,[29][30][31]. Qin et al [21] investigate the diversified top-k search results in a graph.…”