ASNets: A Benchmark Dataset of Aligned Social Networks for Cross-Platform User Modeling

ABSTRACT

Aligning heterogeneous online social networks is a highly beneficial task proposed in recent years. It targets at automatically aligning accounts from multiple networks by whether they are held by the same natural person. Aligning the networks can improve personalized services by cross-platform user modeling, and is the prerequisite for cross-network analysis. However, there is currently no public benchmark dataset available due to its recency. As performances of this task depend highly on the dataset, experiments using different private datasets are not directly comparable. Therefore, in this paper we propose ASNets, a benchmark dataset with two sets of aligned social networks. With this dataset, we can now properly evaluate different approaches and compare them fairly. The two sets of aligned networks have 328,224 and 141,614 aligned users respectively, covering multilingual usage (Chinese and English) and various types of social networks including general purposed networks, review sites and microblogging sites. We describe the collecting methodology and statistics in details, and evaluate several state-of-the-art network aligning approaches. Beside introducing the dataset, we further propose several potential research directions that benefit from ASNets.

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Index Terms

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing theory, concepts and paradigms

      • Social networks

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

      • Test collections

  • World Wide Web

    • Web searching and information discovery

      • Personalization