SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

Bader, David A.; Madduri, Kamesh

doi:10.1109/ipdps.2008.4536261

Cited by 97 publications

(89 citation statements)

References 35 publications

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“…Given the size of the hostname graph, we used an approximation algorithm implemented by SNAP and set the sampling percentage to 10% of all nodes. This is double the 5% percentage suggested for use in [4]. For more details on the algorithm used, see [3].…”

Section: Robustness Resultsmentioning

confidence: 85%

“…Based on the extracted graphs, we calculated the betweenness centrality for all nodes in both graphs using the Small-world Network Analysis and Partitioning software (SNAP) [4]. Given the size of the hostname graph, we used an approximation algorithm implemented by SNAP and set the sampling percentage to 10% of all nodes.…”

Section: Robustness Resultsmentioning

confidence: 99%

“…The intuition is that if a node lies on many shortest paths it is an important node, since removal of such a node will directly influence the cost of the connectivity between other nodes, as other (i.e., longer) shortest paths will have to be followed. 4 A completely connected network has the maximal robustness, and correspondingly the lowest betweenness centrality: B(n) = 0 for every n ∈ N , and removing one node does not impact the overall connectivity of the network greatly.…”

Section: Measures Of Robustnessmentioning

confidence: 99%

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Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Guéret¹,

Groth²,

Harmelen³

et al. 2010

Lecture Notes in Computer Science

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Abstract. The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, ecommerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We apply these on a substantial subset of it, the 2010 Billion Triple Challenge dataset. We first distinguish the physical structure of the Web of Data from its semantic structure. For both of these structures, we then calculate their robustness, taking betweenness centrality as a robustnessmeasure. To the best of our knowledge, this is the first time that such robustness-indicators have been calculated for the Web of Data. Finally, we determine which links should be added to the Web of Data in order to improve its robustness most effectively. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem. We believe that with this work, we offer an effective method to analyse and improve the most important structure that the Semantic Web community has constructed to date.

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Section: Robustness Resultsmentioning

confidence: 85%

Section: Robustness Resultsmentioning

confidence: 99%

Section: Measures Of Robustnessmentioning

confidence: 99%

See 1 more Smart Citation

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Guéret¹,

Groth²,

Harmelen³

et al. 2010

Lecture Notes in Computer Science

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show abstract

“…Other libraries for high-performance computation on largescale graphs include the Parallel Boost Graph Library (PBGL) [14], the Combinatorial BLAS [7], Georgia Tech's SNAP [4], and the Multithreaded Graph Library [5]. These are all written in C/C++ and with the exception of the PBGL do not include explicit filter support.…”

Section: Related Workmentioning

confidence: 99%

High-Productivity and High-Performance Analysis of Filtered Semantic Graphs

Buluç

Duriakova

Fox

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Abstract-High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry attributes of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest.Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, is customizable in two ways. First, the user can write custom graph algorithms by specifying operations between edges and vertices. These programmer-specified operations are called semiring operations due to KDT's underlying linear-algebraic abstractions. Second, the user can customize existing graph algorithms by writing filters that return true for those vertices and edges the user wants to retain during algorithm execution. For high productivity, both semiring operations and filters are written in a high-level language, resulting in relatively low performance due to the bottleneck of having to call into the Python virtual machine for each vertex and edge.In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine, and show our approach enables users to write in high-level languages and still obtain the high performance of low-level code. We also present a new roofline model for graph traversals, and show that our high-performance implementations do not significantly deviate from the roofline. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs.

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“…We compare the modularity results from Table 1's scale 18 graphs between our parallel implementation and the state-of-the-art implementation in SNAP [2]. Because our parallel matching algorithm is non-deterministic, we use three runs for each P value.…”

Section: Community Qualitymentioning

confidence: 99%

Parallel Community Detection for Massive Graphs

Riedy

Meyerhenke

Ediger

et al. 2012

Parallel Processing and Applied Mathematics

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Abstract. Tackling the current volume of graph-structured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices and nearly 2 billion edges in under 7300 seconds on a massively multithreaded Cray XMT. Our algorithm achieves moderate parallel scalability without sacrificing sequential operational complexity. Community detection partitions a graph into subgraphs more densely connected within the subgraph than to the rest of the graph. We take an agglomerative approach similar to Clauset, Newman, and Moore's sequential algorithm, merging pairs of connected intermediate subgraphs to optimize different graph properties. Working in parallel opens new approaches to high performance. On smaller data sets, we find the output's modularity compares well with the standard sequential algorithms.

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SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

Abstract: We present SNAP (Small-world

Cited by 97 publications

References 35 publications

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

High-Productivity and High-Performance Analysis of Filtered Semantic Graphs

Parallel Community Detection for Massive Graphs

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