Graph Summarization

  title={Graph Summarization},
  author={Angela Bonifati and Stefania Dumbrava and Haridimos Kondylakis},
The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to… 
Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs
The experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about $50%$ of the graph database changes.
Time and Memory Efficient Algorithm for Structural Graph Summaries over Evolving Graphs
Overall the experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms its batch counterpart, even when about 50% of the graph database changes.
BigGraphVis: Leveraging Streaming Algorithms and GPU Acceleration for Visualizing Big Graphs
BigGraphVis is introduced, a new parallel graph visualization method that uses GPU parallel processing and community detection algorithm and probabilistic data structure to leverage parallel processing of Graphics Processing Unit (GPU).
Single-Purpose Algorithms vs. a Generic Graph Summarizer for Computing k-Bisimulations on Large Graphs
The results show that the generic BRS algorithm outperforms the respective native bisimulation algorithms for any value of 𝑘 and opens a new path for efficiently computing bisimulations on large graphs.
Time and Memory Efficient Parallel Algorithm for Structural Graph Summaries and two Extensions to Incremental Summarization and $k$-Bisimulation for Long $k$-Chaining
This paper proves that the incremental algorithm is correct and shows that updates are performed in time O(Δ · d ) , where Δ is the number of additions, deletions, and modifications to the input graph, d the maximum degree, and k is the maximum distance in the subgraphs considered.


Graph Summarization Methods and Applications
This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data, and categorizes summarization approaches by the type of graphs taken as input and further organize each category by core methodology.
Scalable dynamic graph summarization
This work proposes two online, distributed, and tunable algorithms for summarizing large-scale dynamic graphs, based on grouping the nodes of the graph in supernodes according to their connectivity and communication patterns.
Utility-Driven Graph Summarization
This paper explores how to summarize and compress a graph while ensuring that its utility or usefulness does not drop below a certain user-specified utility threshold, and proposes a novel iterative utility-driven graph summarization approach.
Fast and Accurate Graph Stream Summarization
A novel Graph Stream Sketch (GSS for short) is proposed to summarize the graph streams, which has linear space cost O(|E|) (E is the edge set of the graph) and constant update time cost (O(1)) and supports most kinds of queries over graph streams with the controllable errors.
gSketch: On Query Estimation in Graph Streams
This paper proposes a new graph sketch method, gSketch, which combines well studied synopses for traditional data streams with a sketch partitioning technique, to estimate and optimize the responses to basic queries on graph streams.
SWeG: Lossless and Lossy Summarization of Web-Scale Graphs
SWeG is proposed, a fast parallel algorithm for summarizing graphs with compact representations designed for not only shared-memory but also MapReduce settings to summarize graphs that are too large to fit in main memory.
Graph summarization with quality guarantees
This work develops the first polynomial-time approximation algorithms to compute the best possible summary of a certain size under both measures of the original graph.
Geographic Knowledge Graph Summarization
  • Bo Yan
  • Computer Science
    Studies on the Semantic Web
  • 2019
The main contribution of this dissertation is that it introduces the general concept of geospatial inductive bias and explains different ways this idea can be used in the geographic knowledge graph summarization task.
RDF graph summarization: principles, techniques and applications
This tutorial presents a structured analysis and comparison existing works in the area of RDF summarization, based upon a recent survey, and identifies the most pertinent summarization method for different usage scenarios.
Approximate Querying on Property Graphs
It is proved the intractability of the optimal graph summarization problem, under the algorithm’s conditions, and the compactness of the obtained summaries as well as the accuracy of answering counting recursive queries on property graphs.