
SSumM: Sparse Summarization of Massive Graphs
Given a graph G and the desired size k in bits, how can we summarize G w...
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Incremental Lossless Graph Summarization
Given a fully dynamic graph, represented as a stream of edge insertions ...
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Scalable Approximation Algorithm for Graph Summarization
Massive sizes of realworld graphs, such as social networks and web grap...
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Abstractive Query Focused Summarization with QueryFree Resources
The availability of largescale datasets has driven the development of n...
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Approximate Summaries for Why and Whynot Provenance (Extended Version)
Why and whynot provenance have been studied extensively in recent years...
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Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which...
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RealTime Web Scale Event Summarization Using Sequential Decision Making
We present a system based on sequential decision making for the online s...
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UtilityBased Graph Summarization: New and Improved
A fundamental challenge in graph mining is the everincreasing size of datasets. Graph summarization aims to find a compact representation resulting in faster algorithms and reduced storage needs. The flip side of graph summarization is the loss of utility which diminishes its usability. The key questions we address in this paper are: (1)How to summarize a graph without any loss of utility? (2)How to summarize a graph with some loss of utility but above a userspecified threshold? (3)How to query graph summaries without graph reconstruction? We also aim at making graph summarization available for the masses by efficiently handling webscale graphs using only a consumergrade machine. Previous works suffer from conceptual limitations and lack of scalability. In this work, we make three key contributions. First, we present a utilitydriven graph summarization method, based on a clique and independent set decomposition, that produces significant compression with zero loss of utility. The compression provided is significantly better than stateoftheart in lossless graph summarization, while the runtime is two orders of magnitude lower. Second, we present a highly scalable algorithm for the lossy case, which foregoes the expensive iterative process that hampers previous work. Our algorithm achieves this by combining a memory reduction technique and a novel binarysearch approach. In contrast to the competition, we are able to handle webscale graphs in a single machine without a performance impediment as the utility threshold (and size of summary) decreases. Third, we show that our graph summaries can be used asis to answer several important classes of queries, such as triangle enumeration, Pagerank, and shortest paths. This is in contrast to other works that incrementally reconstruct the original graph for answering queries, thus incurring additional time costs.
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