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Titlebook: Web Information Systems Engineering -- WISE 2013; 14th International C Xuemin Lin,Yannis Manolopoulos,Guangyan Huang Conference proceedings

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51#
發(fā)表于 2025-3-30 09:02:17 | 只看該作者
Propagated Opinion Retrieval in Twitter major challenge to using them effectively. Here we consider the problem of finding propagated opinions – tweets that express an opinion about some topics, but will be retweeted. Within a learning-to-rank framework, we explore a wide of spectrum features, such as retweetability, opinionatedness and
52#
發(fā)表于 2025-3-30 15:14:37 | 只看該作者
Diversifying Tag Selection Result for Tag Clouds by Enhancing both Coverage and Dissimilarityited number of representative tags from a large set of tags, is the core task for creating tag clouds. Diversity of tag selection result is an important factor that affects user satisfaction. Information coverage and item dissimilarity are two major perspectives for exploring the concept of diversit
53#
發(fā)表于 2025-3-30 20:37:42 | 只看該作者
54#
發(fā)表于 2025-3-31 00:25:43 | 只看該作者
55#
發(fā)表于 2025-3-31 01:12:07 | 只看該作者
56#
發(fā)表于 2025-3-31 05:47:34 | 只看該作者
Community Detection in Social Media by Leveraging Interactions and Intensitiesst expressed on a topic. In this paper we present a community detection approach for user interaction networks which exploits both their structural properties and intensity patterns. The proposed approach builds on existing graph clustering methods that identify both communities of nodes, as well as
57#
發(fā)表于 2025-3-31 11:57:15 | 只看該作者
Community Detection in Social Media by Leveraging Interactions and Intensitiesst expressed on a topic. In this paper we present a community detection approach for user interaction networks which exploits both their structural properties and intensity patterns. The proposed approach builds on existing graph clustering methods that identify both communities of nodes, as well as
58#
發(fā)表于 2025-3-31 13:58:04 | 只看該作者
A Novel and Model Independent Approach for Efficient Influence Maximization in Social Networks whom .. The presence of both . and . in these datasets pose new challenges while conducting social network analysis. In particular, we present a general framework to deal with both variety and volume in the data for a key social network analysis task - Influence Maximization. The well known influen
59#
發(fā)表于 2025-3-31 18:18:55 | 只看該作者
A Novel and Model Independent Approach for Efficient Influence Maximization in Social Networks whom .. The presence of both . and . in these datasets pose new challenges while conducting social network analysis. In particular, we present a general framework to deal with both variety and volume in the data for a key social network analysis task - Influence Maximization. The well known influen
60#
發(fā)表于 2025-3-31 21:48:07 | 只看該作者
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