Article Source
Individual and Collective Graph Mining: Principles, Algorithms and Applications
Authors: Danai Koutra, Christos Faloutsos
Link: https://www.morganclaypool.com/doi/10.2200/S00796ED1V01Y201708DMK014
Keywords: data mining, graph mining and exploration, graph similarity, graph matching, network alignment, graph summarization, pattern mining, outlier detection, anomaly detection, scalability, fast algorithms, visualization, social networks, brain graphs, connectomes
Morgan-Claypool Book
Citation (bibtex):
@book{KoutraF17,
author = {Danai Koutra and
Christos Faloutsos},
title = {Individual and Collective Graph Mining: Principles, Algorithms and Applications},
publisher = {Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool},
year = {2017},
pages = {206}
}
Chapter 2: Summarization of Static Graphs
Chapter 3: Inference in a Graph
Two Classes
Multiple Classes
More detailed description & derivations
Full paper with all the proofs
Chapter 4: Summarization of Dynamic Graphs
TimeCrunch code (Matlab / Python)
Chapter 5: Graph Similarity
Tutorial slides at SDM’14 & ICDM’14
Chapter 6: Graph Alignment
Tutorial slides at SDM’14 & ICDM’14