SsAG: Summarization and sparsification of Attributed Graphs
Sarwan Ali, Muhammad Ahmad, Maham Anwer Beg, Imdad Ullah Khan,, Safiullah Faizullah, Muhammad Asad Khan

TL;DR
SsAG is a scalable graph summarization method that efficiently creates sparse, attribute-aware summaries of large graphs by merging nodes iteratively, minimizing reconstruction error and maximizing attribute homogeneity.
Contribution
The paper introduces SsAG, a novel scalable algorithm for lossy attributed graph summarization that uses a closed-form merging criterion and weighted sampling for efficiency.
Findings
SsAG is up to 5 times faster than existing methods.
Generates summaries with comparable quality to state-of-the-art.
Reduces storage cost with minimal increase in error.
Abstract
We present SsAG, an efficient and scalable lossy graph summarization method that retains the essential structure of the original graph. SsAG computes a sparse representation (summary) of the input graph and also caters to graphs with node attributes. The summary of a graph is stored as a graph on supernodes (subsets of vertices of ), and a weighted superedge connects two supernodes. The proposed method constructs a summary graph on supernodes that minimize the reconstruction error (difference between the original graph and the graph reconstructed from the summary) and maximum homogeneity with respect to attributes. We construct the summary by iteratively merging a pair of nodes. We derive a closed-form expression to efficiently compute the reconstruction error after merging a pair and approximate this score in constant time. To reduce the search space for selecting the best…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph Theory and Algorithms · Advanced Graph Neural Networks · Data Management and Algorithms
