What are some good papers on data mining in graphs?

There are several influential and informative papers on data mining in graphs, covering various aspects such as graph mining, graph-based machine learning, and network analysis. Here are some notable papers in this field:

1. "Mining Graph Data" by Diane J. Cook and Lawrence B. Holder

   • This paper provides a comprehensive overview of graph mining techniques, including frequent subgraph mining and graph classification.

2. "Graph-Based Semi-Supervised Learning" by Xiaojin Zhu

   • The paper explores the use of graph-based semi-supervised learning methods, which are commonly applied to scenarios where labeled data is scarce.

3. "Fast Random Walk with Restart and Its Applications" by Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan

   • The authors introduce a fast algorithm for random walk with restart, a fundamental operation in graph-based data mining, and discuss its applications.

4. "Community Structure in Social and Biological Networks" by M. E. J. Newman

   • This paper discusses the detection and analysis of community structures in networks, a crucial aspect of understanding the organization of graphs.

5. "PageRank Beyond the Web" by Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, and Gene H. Golub

   • The paper discusses the extension of Google's PageRank algorithm to applications beyond web link analysis, including ranking nodes in graphs for various purposes.

6. "Graph-Based Anomaly Detection" by Varun Chandola, Arindam Banerjee, and Vipin Kumar

   • The authors explore graph-based anomaly detection techniques, focusing on identifying unusual patterns in graph-structured data.

7. "Graph Mining in a Nutshell" by Charu C. Aggarwal

   • This survey paper provides an overview of graph mining techniques, covering topics such as frequent subgraph mining, graph classification, and clustering.

8. "DeepWalk: Online Learning of Social Representations" by Bryan Perozzi, Rami Al-Rfou, and Steven Skiena

   • The paper introduces DeepWalk, a method for learning representations of nodes in a graph, particularly useful for large-scale graphs and social network analysis.

9. "GraphSAGE: Inductive Representation Learning on Large Graphs" by William L. Hamilton, Rex Ying, and Jure Leskovec

   • The authors propose GraphSAGE, a method for inductive representation learning on large graphs, enabling the generalization of learned representations to unseen nodes.

10. "The Link Prediction Problem for Social Networks" by David Liben-Nowell and Jon Kleinberg

    • The paper addresses the link prediction problem in social networks, discussing methods to predict future connections between nodes in evolving graphs.

These papers cover a range of topics within the field of data mining in graphs, and they can serve as valuable resources for researchers, practitioners, and students interested in this area.