DeepAI AI Chat
Log In Sign Up

Parameter-free Structural Diversity Search

by   Jinbin Huang, et al.
Hong Kong Baptist University
Wuhan University

The problem of structural diversity search is to nd 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. Speci cally, 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 parameter-free structural diversity search in this paper. An e cient 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.


Truss-based Structural Diversity Search in Large Graphs

Social decisions made by individuals are easily influenced by informatio...

Reliable Community Search in Dynamic Networks

Local community search is an important research topic to support complex...

Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

Existing temporal community search suffers from two defects: (i) they ig...

Best-k Search Algorithm for Neural Text Generation

Modern natural language generation paradigms require a good decoding str...

Building large k-cores from sparse graphs

A popular model to measure network stability is the k-core, that is the ...

Solving MAP Exactly using Systematic Search

MAP is the problem of finding a most probable instantiation of a set of ...

On Feature Diversity in Energy-based Models

Energy-based learning is a powerful learning paradigm that encapsulates ...