Weiwei Hu

Jiangsu University

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Yihua Huang

Nanjing University

Zhaokang Wang

Nanjing University of Aeronautics and Astronautics

Rong Gu

Nanjing University

Chunfeng Yuan

Nanjing University

Lidong He

Nanjing University of Science and Technology

Guanxue Yang

Jiangsu University

Chunfeng Yuan

Chinese Academy of Sciences

Weili Xu

Zhejiang Institute of Hydraulics & Estuary

Jiandong Bao

Nanjing University of Science and Technology

Liya Dou

Beijing University of Chemical Technology

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Nanjing University

Nanjing University of Science and Technology

Shanghai Jiao Tong University

Shandong Institute of Automation

Chinese Academy of Sciences

University of Chinese Academy of Sciences

East China Normal University

Sun Yat-sen University

Beijing University of Posts and Telecommunications

Minzu University of China

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BENU: Distributed Subgraph Enumeration with Backtracking-Based Framework

2022 IEEE 38th International Conference on Data Engineering (ICDE) (2019)

Zhaokang Wang Rong Gu Weiwei Hu Chunfeng Yuan Yihua Huang

Given a small pattern graph and a large data graph, the task of subgraph enumeration is to find all the subgraphs of the data graph that are isomorphic to the pattern graph. The state-of-the-art distributed algorithms like SEED and CBF turn subgraph enumeration into a distributed multi-way join problem. They are inefficient in communication as they have to shuffle partial matching results that are much larger than the data graph itself during the join. They also spend non-trivial costs on constructing indexes for data graphs. Different from those join-based algorithms, we develop a new backtracking-based framework BENU for distributed subgraph enumeration. BENU divides a subgraph enumeration task into a group of local search tasks that can be executed in parallel. Each local search task follows a backtracking-based execution plan to enumerate subgraphs. The data graph is stored in a distributed database and is queried as needed. BENU only queries the necessary edges of the data graph and avoids shuffling partial matching results. We also develop an efficient implementation for BENU. We set up an in-memory database cache on each machine. Taking advantage of the inter-task and intra-task locality, the cache significantly reduces the communication cost with controllable memory usage. We conduct extensive experiments to evaluate the performance of BENU. The results show that BENU is scalable and outperforms the state-of-the-art methods by up to an order of magnitude.

Backtracking

Enumeration

Factor-critical graph

Distributed memory

10.1109/icde.2019.00021

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Distributed Subgraph Enumeration via Backtracking-based Framework

arXiv (Cornell University) (2020)

Zhaokang Wang Weiwei Hu Chunfeng Yuan Rong Gu Yihua Huang

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. The state-of-the-art distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework (B-BENU) for distributed subgraph enumeration. B-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. B-BENU does not shuffle any partial matching result. Instead, it stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand. To support dynamic data graphs, we propose the concept of incremental pattern graphs and turn continuous subgraph enumeration into enumerating incremental pattern graphs at each time step. We develop the Streaming-BENU framework (S-BENU) to enumerate their matches efficiently. We implement B-BENU and S-BENU with the local database cache and the task splitting techniques. The extensive experiments show that B-BENU and S-BENU can scale to big data graphs and complex pattern graphs. They outperform the state-of-the-art methods by up to one and two orders of magnitude, respectively.

Enumeration

Backtracking

10.48550/arxiv.2006.12819

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Citations (1)

Towards Efficient Distributed Subgraph Enumeration Via Backtracking-Based Framework

IEEE Transactions on Parallel and Distributed Systems (2021)

Zhaokang Wang Weiwei Hu Guowang Chen Chunfeng Yuan Rong Gu

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. Existing distributed methods are inefficient in communication. They have to shuffle partial matching results during the distributed multiway join. The partial matching results may be much larger than the data graph itself. To overcome the drawback, we develop the Batch-BENU framework for distributed subgraph enumeration on static data graphs. Batch-BENU executes a group of local search tasks in parallel. Each task enumerates subgraphs around a vertex in the data graph, guided by a backtracking-based execution plan. To handle large-scale data graphs that may exceed the memory capacity of a single machine, Batch-BENU stores the data graph in a distributed database. Each task queries adjacency sets of the data graph on demand, shuffling the data graph instead of partial matching results. To support incremental subgraph enumeration on dynamic data graphs, we propose the Streaming-BENU framework. Streaming-BENU turns the problem of enumerating incremental matching results into enumerating all matching results of incremental pattern graphs at each time step. We implement Batch-BENU and Streaming-BENU with the local database cache and the load balance optimization to improve their efficiency. Extensive experiments show that Batch-BENU and Streaming-BENU can scale to big graphs and complex pattern graphs. They outperform the state-of-the-art distributed methods by up to one and two orders of magnitude, respectively.

Enumeration

Backtracking

10.1109/tpds.2021.3076246

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Citations (6)

基于加权子空间投影的谱估计器分辨力研究 (Improving Resolution Ability of Spectral Estimator by Weighted Subspace Projection).

计算机科学 (2015)

Jiandong Bao Weili Xu Weiwei Hu Xiaomin Xie

Oblique projection

Source

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Nonlinear causal network learning via Granger causality based on extreme support vector regression

Chaos An Interdisciplinary Journal of Nonlinear Science (2024)

Guanxue Yang Weiwei Hu Lidong He Liya Dou

For complex networked systems, based on the consideration of nonlinearity and causality, a novel general method of nonlinear causal network learning, termed extreme support vector regression Granger causality (ESVRGC), is proposed. The nonuniform time-delayed influence of the driving nodes on the target node is particularly considered. Then, the restricted model and the unrestricted model of Granger causality are, respectively, formulated based on extreme support vector regression, which uses the selected time-delayed components of system variables as the inputs of kernel functions. The nonlinear conditional Granger causality index is finally calculated to confirm the strength of a causal interaction. Generally, based on the simulation of a nonlinear vector autoregressive model and nonlinear discrete time-delayed dynamic systems, ESVRGC demonstrates better performance than other popular methods. Also, the validity and robustness of ESVRGC are also verified by the different cases of network types, sample sizes, noise intensities, and coupling strengths. Finally, the superiority of ESVRGC is successful verified by the experimental study on real benchmark datasets.

Robustness

Causality

10.1063/5.0183537

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Citations (2)