GraphChallenge.org: Raising the Bar on Graph Analytic Performance

Samsi, Siddharth; Gadepally, Vijay; Hurley, Michael B.; Jones, Michael; Kao, Edward K.; Mohindra, Sanjeev; Monticciolo, Paul; Reuther, Albert; Smith, Steven T.; Song, William; Staheli, Diane; Kepner, Jeremy

doi:10.1109/hpec.2018.8547527

Cited by 15 publications

(10 citation statements)

References 57 publications

(47 reference statements)

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“…Two relevant results on truss decomposition are from the IEEE MIT HPEC GraphChallenge 2018 [27]: the champion [25] is based on a high-performance distributed algorithm (hereafter called PS18) and one of the finalists [22] is based on a high-performance collaborative (GPU+CPU) algorithm (hereafter called MD+18). Another relevant result is a winner of the Student Innovation Awards in the GraphChallenge of the following year [2], which also presents a GPU-based approach for truss decomposition and computing the max truss (hereafter called AA+19).…”

Section: B Indirect Comparisonmentioning

confidence: 99%

Truly Scalable K-Truss and Max-Truss Algorithms for Community Detection in Graphs

et al. 2020

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The notion of k-truss has been introduced a decade ago in social network analysis and security for community detection, as a form of cohesive subgraphs less stringent than a clique (set of pairwise linked nodes), and more selective than a k-core (induced subgraph with minimum degree k). A k-truss is an inclusion-maximal subgraph H in which each edge belongs to at least k − 2 triangles inside H . The truss decomposition establishes, for each edge e, the maximum k for which e belongs to a k-truss. Analogously to the largest clique and to the maximum k-core, the strongest community for k-truss is the max-truss, which corresponds to the k-truss having the maximum k. Even though the computation of truss decomposition and of the max-truss takes polynomial time, on a large scale, it suffers from handling a potentially cubic number of wedges. In this paper, we provide a new algorithm FMT, which advances the state of the art on different sides: lower execution time, lower memory usage, and no need for expensive hardware. We compare FMT experimentally with the most recent state-of-the-art algorithms on a set of large real-world and synthetic networks with over a billion edges. The massive improvement allows FMT to compute the max-truss of networks of tens of billions of edges on a single standard server machine.

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Section: B Indirect Comparisonmentioning

confidence: 99%

Truly Scalable K-Truss and Max-Truss Algorithms for Community Detection in Graphs

et al. 2020

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“…Network motif enumeration in community detection is to identify and count a given subgraph (related to subgraph isomorphism problem in computer science). This paper studies its special case when the subgraph is particularly a triangle [14]. Other subgraphs such as clique can also be counted by MEGA with different verification schemes.…”

Section: Preliminaries Of Network Motif Countingmentioning

confidence: 99%

“…We compare MEGA with the algorithm in [39], which is an award-winning work of the MIT/Amazon/IEEE Graph Challenge [14]. The algorithm in [39] assigns direction to each edge based on the degree of each vertex.…”

Section: Evaluation On Real-world Networkmentioning

confidence: 99%

MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

Hang

Ling³

et al. 2020

Preprint

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Statistical network analysis plays a critical role in managing the coronavirus disease (COVID-19) infodemic such as addressing community detection and rumor source detection problems in social networks. As the data underlying infodemiology are fundamentally huge graphs and statistical in nature, there are computational challenges to the design of graph algorithms and algorithmic speedup. A framework that leverages cloud computing is key to designing scalable data analytics for infodemic control. This paper proposes the MEGA framework, which is a novel joint hierarchical clustering and parallel computing technique that can be used to process a variety of computational tasks in large graphs. Its unique feature lies in using statistical machine learning to exploit the inherent statistics of data to accelerate computation. Our MEGA framework consists of first pruning, followed by hierarchical clustering based on geodesic distance and then parallel computing, lending itself readily to parallel computing software, e.g., MapReduce or Hadoop. In particular, we illustrate how our MEGA framework computes two representative graph problems for infodemic control, namely network motif counting for community detection and network centrality computation for rumor source detection. Interesting special cases of optimal tuning in the MEGA framework are identified based on geodesic distance characterization and random graph model analysis. Finally, we evaluate its performance using cloud software implementation and real-world graph datasets to demonstrate its computational efficiency over existing state of the art.

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“…Different from the algorithms proposed in the IEEE HPEC static graph challenge using high performance CPU or GPU to boost performance [50], our focus is to investigate if it is viable to efficiently and economically compute the k-trusses of large networks on a single consumer-grade machine. Therefore, the memory usage by the program is our major concern.…”

Section: Truss Decompositionmentioning

confidence: 99%

Graph-XLL: a Graph Library for Extra Large Graph Analytics on a Single Machine

Srinivasan

Thomo

2019

2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)

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Graph libraries containing already-implemented algorithms are highly desired since users can conveniently use the algorithms off-the-shelf to achieve fast analytics and prototyping, rather than implementing the algorithms with lower-level APIs. Besides the ease of use, the ability to efficiently process extra large graphs is also required by users. The popular existing graph libraries include the igraph R library and the NetworkX Python library. Although these libraries provide many off-the-shelf algorithms for users, the in-memory graph representation limits their scalability for computing on large graphs. Therefore, in this work, we develop Graph-XLL: a graph library implemented using the WebGraph framework in a vertex-centric manner, with much less memory requirement compared to igraph and NetworkX. Scalable analytics for extra large graphs (up to tens of millions of vertices and billions of edges) can be achieved on a single consumer grade machine within a reasonable amount of time. Such computation would cause out-of-memory error if using igraph or NetworkX.

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GraphChallenge.org: Raising the Bar on Graph Analytic Performance

Cited by 15 publications

References 57 publications

Truly Scalable K-Truss and Max-Truss Algorithms for Community Detection in Graphs

Truly Scalable K-Truss and Max-Truss Algorithms for Community Detection in Graphs

MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

Graph-XLL: a Graph Library for Extra Large Graph Analytics on a Single Machine

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