Who With Whom And How?: Extracting Large Social Networks Using Search Engines
This work addresses the scalability issue in social network extraction for applications like identifying influential entities and detecting communities, though it appears incremental as it builds on existing query-based approaches with iterative improvements.
The paper tackles the problem of extracting large social networks from unstructured web content, which existing methods do not scale well for, by introducing novel query-based search engine mining methodologies, resulting in high-quality, scalable, and efficient construction of social graphs as demonstrated in experimental evaluations across different domains.
Social network analysis is leveraged in a variety of applications such as identifying influential entities, detecting communities with special interests, and determining the flow of information and innovations. However, existing approaches for extracting social networks from unstructured Web content do not scale well and are only feasible for small graphs. In this paper, we introduce novel methodologies for query-based search engine mining, enabling efficient extraction of social networks from large amounts of Web data. To this end, we use patterns in phrase queries for retrieving entity connections, and employ a bootstrapping approach for iteratively expanding the pattern set. Our experimental evaluation in different domains demonstrates that our algorithms provide high quality results and allow for scalable and efficient construction of social graphs.