GS4: Graph stream summarization based on both the structure and semantics

Payaman, N. A. Ashrafi; Kangavari, Mohammad Reza; Hosseini, Saeid; Fander, Amir Mohammad

doi:10.1007/s11227-020-03290-2

Cited by 5 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, MoSSo groups similar nodes and incrementally calculates a lossless summary, while gSketch [19] estimates edge frequency to generate a lossy summary that supports structural queries. GS4 [10] generates a lossy summary using the sliding window model and vertex properties of the graph stream. In literature [11], a compressed binary tree corresponds to the streaming graph data for lossy summarization.…”

Section: Streaming Graph Summarizationmentioning

confidence: 99%

An Efficient Lossless Graph Summarization Method for Large Streaming Graphs

Wang,

Wang

2023

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Graph summarization aims to extract critical information from large graphs by creating summaries that represent the original data. Especially, real-world daily applications generate massive dynamic streaming graphs, representing as edge or node streams. How to efficiently generate compact and lossless graph summaries for large streaming graphs is still a challenging problem. This paper proposes an efficient and scalable lossless summarization method for streaming graphs, called Partition and Similarity-based Two-Stage Summarization (PSTSS). PSTSS uses a streaming graph partitioning algorithm as the first stage to generate coarse-grained summaries by on-the-fly subgraph partitioning. The second stage generates fine-grained supernodes within these coarse-grained supernodes through efficient node similarity calculation. Our experiments on six large datasets demonstrate that our method achieves better operational efficiency, compression rate, scalability, and graph query speed compared to state-of-the-art lossless summarization algorithms.

show abstract

Section: Streaming Graph Summarizationmentioning

confidence: 99%

An Efficient Lossless Graph Summarization Method for Large Streaming Graphs

Wang,

Wang

2023

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…Therefore, the partitioning of the network should be based on the object distribution and the network topology. Moreover, the partitioning of the line graph will consider the the structure and vertex attributes of the graph [41]. The network is partitioned into a series of sub-network regions that are balanced with respect to the number of SOs in each region (cf.…”

Section: Graph Partitioningmentioning

confidence: 99%

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

Min

Pfoser

Züfle

et al. 2021

IJGI

View full text Add to dashboard Cite

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods.

show abstract

$\mathcal{FR}_{B}$-Sketch: A Graph Stream Summarization Model Based on Pattern and Rank

Chen,

Wen,

2024

2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)

View full text Add to dashboard Cite

GS4: Graph stream summarization based on both the structure and semantics

Cited by 5 publications

References 37 publications

An Efficient Lossless Graph Summarization Method for Large Streaming Graphs

An Efficient Lossless Graph Summarization Method for Large Streaming Graphs

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

$\mathcal{FR}_{B}$-Sketch: A Graph Stream Summarization Model Based on Pattern and Rank

Contact Info

Product

Resources

About