scholarly journals Top-$k$ Subgraph Query Based on Frequent Structure in Large-Scale Dynamic Graphs

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 78471-78482
Author(s):  
Xiaohuan Shan ◽  
Guangxiang Wang ◽  
Linlin Ding ◽  
Baoyan Song ◽  
Yan Xu
Keyword(s):  
Large Scale ◽  
Dynamic Graphs ◽  
Subgraph Query

scholarly journals Adaptive Partitioning for Large-Scale Dynamic Graphs

2014 ◽  
Author(s):  
Luis M. Vaquero ◽  
Felix Cuadrado ◽  
Dionysios Logothetis ◽  
Claudio Martella
Keyword(s):  
Large Scale ◽  
Dynamic Graphs ◽  
Adaptive Partitioning

scholarly journals Dynamic Top-K Interesting Subgraph Query on Large-Scale Labeled Graphs

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 61
Author(s):  
Xiaohuan Shan ◽  
Chunjie Jia ◽  
Linlin Ding ◽  
Xingyan Ding ◽  
Baoyan Song
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Maintenance Strategy ◽  
Incremental Maintenance ◽  
Labeled Graph ◽  
Important Means ◽  
Subgraph Query ◽  
Redundant Operation ◽  
The Individual ◽  
Optimization Mechanism

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads.

scholarly journals Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xiaohuan Shan ◽  
Haihai Li ◽  
Chunjie Jia ◽  
Dong Li ◽  
Baoyan Song
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Original Data ◽  
The State ◽  
Average Performance ◽  
Query Graph ◽  
Subgraph Query ◽  
Data Graph ◽  
Partition Method ◽  
The Impact

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.

scholarly journals Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248764
Author(s):  
Angelo Furno ◽  
Nour-Eddin El Faouzi ◽  
Rajesh Sharma ◽  
Eugenio Zimeo
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Large Scale ◽  
Transportation Networks ◽  
Smart Cities ◽  
Dynamic Networks ◽  
Computation Time ◽  
Weighted Graphs ◽  
Dynamic Graphs ◽  
Physical Infrastructure

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to identify the components (nodes) of a system modelled as a graph that are mostly traversed by information flows thus being critical to the proper functioning of the system itself. In the transportation domain, the metric has been mainly adopted to discover topological bottlenecks of the physical infrastructure composed of roads or railways. The adoption of this metric to study the evolution of transportation networks that take into account also the dynamic conditions of traffic is in its infancy mainly due to the high computation time needed to compute BC in large dynamic graphs. This paper explores the adoption of dynamic BC, i.e., BC computed on dynamic large-scale graphs, modeling road networks and the related vehicular traffic, and proposes the adoption of a fast algorithm for ahead monitoring of transportation networks by computing approximated BC values under time constraints. The experimental analysis proves that, with a bounded and tolerable approximation, the algorithm computes BC on very large dynamically weighted graphs in a significantly shorter time if compared with exact computation. Moreover, since the proposed algorithm can be tuned for an ideal trade-off between performance and accuracy, our solution paves the way to quasi real-time monitoring of highly dynamic networks providing anticipated information about possible congested or vulnerable areas. Such knowledge can be exploited by travel assistance services or intelligent traffic control systems to perform informed re-routing and therefore enhance network resilience in smart cities.

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