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Titlebook: Social Media Processing; 5th National Confere Yuming Li,Guoxiong Xiang,Mingwen Wang Conference proceedings 2016 Springer Nature Singapore P

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11#
發(fā)表于 2025-3-23 13:25:49 | 只看該作者
Extraction of Expert Relations Integrated with Expert Topic and Associated Relationship Features,odel of expert relations by integrating expert topics and associated relationship features. The experimental results have demonstrated that the proposed method that integrated with expert topic and the associated relationship features of experts supports the extraction of expert relations and shows promising performance.
12#
發(fā)表于 2025-3-23 17:06:30 | 只看該作者
13#
發(fā)表于 2025-3-23 20:16:32 | 只看該作者
14#
發(fā)表于 2025-3-23 23:06:33 | 只看該作者
Learning Cost-Effective Social Embedding for Cascade Prediction,d temporal features for prediction, or explicitly rely on particular information diffusion models. Recently, researchers attempt to design fully data-driven methods for cascade prediction (i.e., without requiring human-defined features or information diffusion models), directly leveraging historical
15#
發(fā)表于 2025-3-24 03:57:08 | 只看該作者
Missing and Spurious Interactions in Heterogeneous Military Networks,often incomplete and noisy, where missing links prediction methods and spurious links identification algorithms can be applied. Military organizations could be modeled as heterogeneous complex networks, where nodes represent different types of functional units and edges denote different types of com
16#
發(fā)表于 2025-3-24 09:05:09 | 只看該作者
17#
發(fā)表于 2025-3-24 12:31:24 | 只看該作者
18#
發(fā)表于 2025-3-24 16:23:16 | 只看該作者
Efficient Community Detection Based on Label Propagation with Belonging Coefficient and Edge Probabe proposed, but the performance, stability and time complexity of them still need to be improved. In this paper, we investigate the modularity-specialized label propagation algorithm (LPAm), and find that the time complexity of LPAm greatly increased. We prune the LPAm algorithm by only considering
19#
發(fā)表于 2025-3-24 22:39:40 | 只看該作者
A Novel Approach for Relation Extraction with Few Labeled Data,sed to address this issue, it suffers from massive noise and the trained model cannot be applied to unseen relations. We present a novel approach for relation extraction which uses the relation definition as a guide and only needs a hundred of high-quality mention examples for training model. In det
20#
發(fā)表于 2025-3-25 03:13:08 | 只看該作者
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