Shared-Memory Graph Truss Decomposition

Kabir, Humayun; Madduri, Kamesh

doi:10.1109/hipc.2017.00012

Cited by 23 publications

(13 citation statements)

References 34 publications

(61 reference statements)

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“…They present speedups to 23.6x on friendster graph with 72 threads. Regarding the k-truss decomposition, the HPEC challenge [35] attracted interesting studies that parallelize the computation [41,45,22]. In particular, Shaden et al [41] reports competitive results with respect to the earlier version of our work [36].…”

Section: Scalability and Comparison With Peelingmentioning

confidence: 74%

“…Our speedup numbers increase with more threads and faster solutions are possible with more cores. Recent results: There is a couple recent studies, concurrent to our work, that introduced new efficient parallel algorithms for k-core [8] and k-truss [41,45,22] decompositions. Dhulipala et al [8] have a new parallel bucket data structure for k-core decomposition that enables work-efficient parallelism, which is not possible with our algorithms.…”

Section: Scalability and Comparison With Peelingmentioning

confidence: 87%

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Local algorithms for hierarchical dense subgraph discovery

2018

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Finding the dense regions of a graph and relations among them is a fundamental problem in network analysis. Core and truss decompositions reveal dense subgraphs with hierarchical relations. The incremental nature of algorithms for computing these decompositions and the need for global information at each step of the algorithm hinders scalable parallelization and approximations since the densest regions are not revealed until the end. In a previous work, Lu et al. proposed to iteratively compute the h-indices of neighbor vertex degrees to obtain the core numbers and prove that the convergence is obtained after a finite number of iterations. This work generalizes the iterative h-index computation for truss decomposition as well as nucleus decomposition which leverages higher-order structures to generalize core and truss decompositions. In addition, we prove convergence bounds on the number of iterations. We present a framework of local algorithms to obtain the core, truss, and nucleus decompositions. Our algorithms are local, parallel, offer high scalability, and enable approximations to explore time and quality trade-offs. Our shared-memory implementation verifies the efficiency, scalability, and effectiveness of our local algorithms on real-world networks.

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Section: Scalability and Comparison With Peelingmentioning

confidence: 74%

Section: Scalability and Comparison With Peelingmentioning

confidence: 87%

Local algorithms for hierarchical dense subgraph discovery

2018

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“…However, for large graphs, the hash table is expensive to use and designing an optimum hash function is not a trivial problem. The second type of algorithms use more advanced parallelization techniques on high-performance multi-core machines to significantly reduce the runtime [46][47][48][49]. Memory usage is not the major concern for these parallel programs since they are designed for high-performance machines, which are usually capable of keeping the whole graph as well as the hash table in the main memory.…”

Section: Truss Decompositionmentioning

confidence: 99%

“…However, the cost for the hardware is high. For algorithms that avoid using the hash table (e.g., [46] uses an array-based alternative), we can still find room for optimization on the data structure design to use the memory more efficiently. In short, both the serial and the parallel algorithms have limitations.…”

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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“…Other works on parallel algorithms for enumerating dense subgraphs from a massive graph include parallel algorithms for enumerating k-cores [39], [40], [41], [42], k-trusses [42], [43], [44], nuclei [42], and distributed memory algorithms for enumerating bicliques [45].…”

Section: Related Workmentioning

confidence: 99%

Shared-Memory Parallel Maximal Clique Enumeration

Das

Sanei-Mehri

Tirthapura

2018

2018 IEEE 25th International Conference on High Performance Computing (HiPC)

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We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primitive for identifying dense structures in a graph. Due to its computationally intensive nature, parallel methods are imperative for dealing with large graphs. However, surprisingly, there do not yet exist scalable and parallel methods for MCE on a shared-memory parallel machine. In this work, we present efficient shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing that they can scale to a large number of processors, and (3) our implementations on a multicore machine shows a good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE.

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Shared-Memory Graph Truss Decomposition

Cited by 23 publications

References 34 publications

Local algorithms for hierarchical dense subgraph discovery

Local algorithms for hierarchical dense subgraph discovery

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

Shared-Memory Parallel Maximal Clique Enumeration

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