ArnetMiner

TLDR

The paper presents ArnetMiner, a system designed to extract and mine academic social networks by integrating researcher profiles, publication data, and modeling topical relationships among authors, papers, and venues. ArnetMiner automatically extracts researcher profiles from the Web, integrates publication data from digital libraries, resolves name ambiguity with a probabilistic framework, models the entire academic network, and offers search services such as expertise and association queries. The system has extracted 448,470 researcher profiles and provides expertise and people association search services based on its modeling results.

Abstract

This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.