Social Network Data Analytics

By Charu C. Aggarwal. 2011

530 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f5a059e8f50ae137e0146fbf32e199d8c6dcb83a

How researchers see this paper

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

In Computer Networks and Communications

1. Charu C. Aggarwal
2. Jiawei Han
3. Yizhou Sun
4. Darko Hric
5. Ralf Klinkenberg
6. Stephen Coggeshall
7. Ephraim Nissan
8. Santo Fortunato

In Statistical and Nonlinear Physics

1. Charu C. Aggarwal
2. Darko Hric
3. Yizhou Sun
4. Jiawei Han
5. Ephraim Nissan
6. Santo Fortunato
7. Karthik Subbian
8. Ralf Klinkenberg

Citations in the corpus

• Outlier Analysis (2013, background): “In such cases, community detection algorithms can be enhanced with the use of node content [11].”
• Data Classification: Algorithms and Applications (2014, background): “A detailed survey on collective classification methods may be found in [6].”
• Mining heterogeneous information networks: a structural analysis approach (2013, methodology): “Numerous methods have been developed for the analysis of homogeneous information networks, especially on social networks [1], such as ranking, community detection, link prediction, and influence analysis.”
• Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement (2015, background): “1) stressed the importance of visualization when researching social networks (Aggarwal 2011), advocated the visualization not only of connections within a social network, but also of quantitative data, and pointed out that only a minority of social networks can be mapped onto a geographical…”
• Event Detection in Social Streams (2012, background): “This is related to the problem of community detection [3], in which we try to find structurally connected regions of the social network.”
• Predicting students' final performance from participation in on-line discussion forums (2013, background): “These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines (Aggarwal, 2011).”
• Community detection in networks: A user guide (2016, background): “In particular, the problem of detecting dynamic communities has received a lot of attention (Fortunato, 2010; Spiliopoulou, 2011).”
• Recommender Systems (2016, unknown): “A survey on link prediction methods may be found in [17].”