Abstract
Subgraph counting is a fundamental problem in graph analysis that finds use in a wide array of applications. The basic problem is to count or approximate the occurrences of a small subgraph (the pattern) in a large graph (the dataset). Subgraph counting is a computationally challenging problem, and the last few years have seen a rich literature develop around scalable solutions for it. However, these results have thus far appeared as a disconnected set of ideas that are applied separately by different research groups. We observe that there are a few common algorithmic building blocks that most subgraph counting results build on. In this tutorial, we attempt to summarize current methods through distilling these basic algorithmic building blocks. The tutorial will also cover methods for subgraph analysis on “big data” computational models such as the streaming model and models of parallel and distributed computation.
Highlights
We list papers cited in the introductory slides of the tutorial
Define the graphlet degree to the number of times v appears in that orbit
This is a list of papers using graph orientations for subgraph counting
Summary
We list (most) papers cited in the introductory slides of the tutorial. These are primarily papers on applications of subgraph counting, and a few papers on subgraph counting on attributed graphs. The latter list is by no means comprehensive and merely lists a few papers. A few papers mentioned in the introduction are talked about in detail, during later sections of tutorial (with regards to algorithmic techniques). We do not mention these papers and instead cite them in the later relevant section. [BHLP11]: Result on using 4-cycle for edge weights, to improving community detection based on modularity
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