Corpus ID: 235363981

Parallel Batch-Dynamic k-Core Decomposition

  title={Parallel Batch-Dynamic k-Core Decomposition},
  author={Quanquan C. Liu and Jessica Shi and Shangdi Yu and Laxman Dhulipala and Julian Shun},
Maintaining a k-core decomposition quickly in a dynamic graph is an important problem in many applications, including social network analytics, graph visualization, centrality measure computations, and community detection algorithms. The main challenge for designing efficient k-core decomposition algorithms is that a single change to the graph can cause the decomposition to change significantly. We present the first parallel batch-dynamic algorithm for maintaining an approximate k-core… Expand


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Two new parallel and scalable algorithms for finding the maximal k-core in a graph that use a dynamic graph data structure and avoid one of the largest performance penalties of k- core – pruning vertices and edges are presented. Expand
Incremental k-core decomposition: algorithms and evaluation
This paper proposes a suite of incremental k-core decomposition algorithms for dynamic graph data, and presents incremental algorithms for both insertion and deletion operations, and proposes auxiliary vertex state maintenance techniques that can further accelerate these operations. Expand
Streaming Algorithms for k-core Decomposition
This paper proposes the first incremental k-core decomposition algorithms for streaming graph data, which locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-Core values have to be updated, and efficiently process this subgraph to update the k- core decomposition. Expand
Parallel k-Core Decomposition on Multicore Platforms
  • H. Kabir, Kamesh Madduri
  • Computer Science
  • 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
  • 2017
This work presents a new shared-memory parallel algorithm called PKC for k-core decomposition on multicore platforms by reducing synchronization overhead and creating a smaller graph to process high-degree vertices and shows that PKC consistently outperforms implementations of other methods on a 32-core multicore server and on a collection of large sparse graphs. Expand
K-Core Decomposition of Large Networks on a Single PC
A thorough analysis of all algorithms concluding that it is viable to compute k-core decomposition for large networks in a consumer-grade PC and an optimized implementation of an external-memory algorithm by Cheng, Ke, Chu, and Ozsu is presented. Expand
Distributed algorithms for k-truss decomposition
This paper first improves the existing distributed k-truss decomposition in the MapReduce framework, then proposes a theoretical basis for k- truss and uses it to design an algorithm based on graph-parallel abstractions that significantly outperforms the methods based on Map reduce in terms of running time and disk usage. Expand
Efficient Core Maintenance in Large Dynamic Graphs
The main result is that only certain nodes need to update their core numbers when the graph is changed by inserting/deleting an edge, and an efficient algorithm to identify and recompute the core numbers of such nodes is proposed. Expand
Distributed k-core decomposition and maintenance in large dynamic graphs
This work proposes distributed k-core decomposition and maintenance algorithms for large dynamic graphs, and presents an implementation of the proposed approach on top of the AKKA framework, and experiments show the efficiency of the approach in the case of large dynamic networks. Expand
I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering
The optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption and is the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory. Expand
ParK: An efficient algorithm for k-core decomposition on multicore processors
An experimental analysis of the algorithm of Batagelj and Zaversnik for k-core decomposition is presented and a new algorithm, ParK, is proposed that significantly reduces the working set size and minimizes the random accesses and is compared with state-of-the-art algorithm. Expand