Name
Abstract | ||
|---|---|---|
Researchers usually extract domain experts only through analyzing network structure or partitioning users into several communities according to their label information. Combining structure and content to discovery domain experts is a new attempt. Motivated by that, this paper proposes a domain expert discovery method based on network structure and content semantics, called DEDSC, which can extract authority nodes in overlapping communities. To analyze the overall authority for each user in the social network, two definitions, structure authority value and content authority value, are proposed to evaluate the authority of users in different perspectives. Partitioning users into communities can make the results more accurate. Experimental results show that our proposed method can discover domain experts effectively. In addition, when we need to extract domain experts in a new test dataset, we do not need to re-train the data in the training dataset. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0218488518500277 | INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS |
Keywords | Field | DocType |
Domain experts, structure authority value, content authority value, global centrality | Subject-matter expert,Artificial intelligence,Machine learning,Mathematics,Network structure | Journal |
Volume | Issue | ISSN |
26 | 4 | 0218-4885 |
Citations | PageRank | References |
1 | 0.37 | 13 |
Authors | ||
4 | ||