Combining link and content for community detection: a discriminative approach

By Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu. Knowledge Discovery and Data Mining, 2009

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d15aef555481ed5cd59474e9e2efbc1ee37d5e94

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charu C. Aggarwal
2. Jiliang Tang
3. Huan Liu
4. Xia Hu
5. Jundong Li
6. 刘晗
7. Yi Chang
8. Xiaotong Zhang

In Statistical and Nonlinear Physics

1. Huan Liu
2. Charu C. Aggarwal
3. Jiliang Tang
4. Jundong Li
5. Xia Hu
6. Yi Chang
7. Wei Han
8. Srinivasan Parthasarathy

Citations in the corpus

• Efficient community detection in large networks using content and links (2012, methodology): “PCL-DC takes 234 seconds on this dataset and SA-Cluster-Inc requires 306 seconds.”
• Generalized Latent Factor Models for Social Network Analysis (2011, methodology): “As in [Yang et al., 2009a], we use two paper citation networks, Cora and Citeseer data sets 4 , for evaluation.”
• Attributed Graph Clustering via Adaptive Graph Convolution (2019, background): “Attributed graph clustering [Yang et al., 2009] takes into account both node connectivity and features.”
• Event Detection in Social Streams (2012, neutral): “, 11, 25], these methods are typically designed for static networks.”
• Heterogeneous Network Embedding via Deep Architectures (2015, methodology): “Unlike to traditional linear embedding models [29, 44, 50], the proposed method decomposes the feature learning process into multiple nonlinear layers of a deep architecture.”
• Attributed Network Embedding for Learning in a Dynamic Environment (2017, background): “Œe basic idea is to preserve the node proximity in the embedded Euclidean space, based on which the performance of various network mining tasks such as node classi€cation [3, 5], community detection [34, 41], and link prediction [21, 39] can be enhanced.”
• Clustering attributed graphs: Models, measures and methods (2015, methodology): “Yang et al. (2009) use two validation approaches that are based on ground truth: the normalized MI, briefly described in Section 4.1.1, and the pairwise F measure (PWF).”
• A Survey of Text Clustering Algorithms (2012, methodology): “In the method proposed in [101], the following link-based and content-based steps are combined for effective community detection:”
• Effective Community Search for Large Attributed Graphs (2016, neutral): “identifies communities of comparable or superior quality than those of many existing methods like [21, 32]; and (3) it runs faster than many existing methods.”