TemporalRI: A Subgraph Isomorphism Algorithm for Temporal Networks

2021 ◽  
pp. 675-687
Author(s):  
Giorgio Locicero ◽  
Giovanni Micale ◽  
Alfredo Pulvirenti ◽  
Alfredo Ferro
Keyword(s):  
Subgraph Isomorphism ◽  
Temporal Networks

scholarly journals TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Giovanni Micale ◽  
Giorgio Locicero ◽  
Alfredo Pulvirenti ◽  
Alfredo Ferro
Keyword(s):  
State Of The Art ◽  
Search Space ◽  
The State ◽  
Subgraph Isomorphism ◽  
Temporal Network ◽  
Temporal Networks ◽  
Chronological Order ◽  
Multiple Contacts ◽  
Ri Algorithm ◽  
Target Matching

AbstractTemporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.

Probabilistic Temporal Networks.

1996 ◽  
Author(s):  
Eugene Santos ◽  
Young Jr. ◽  
Joel D.
Keyword(s):  
Temporal Networks

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

scholarly journals Temporal gravity model for important node identification in temporal networks

2021 ◽  
Vol 147 ◽  
pp. 110934
Author(s):  
Jialin Bi ◽  
Ji Jin ◽  
Cunquan Qu ◽  
Xiuxiu Zhan ◽  
Guanghui Wang ◽  
...  
Keyword(s):  
Gravity Model ◽  
Temporal Networks ◽  
Temporal Gravity

Semi-supervised link prediction based on non-negative matrix factorization for temporal networks

2021 ◽  
Vol 145 ◽  
pp. 110769
Author(s):  
Ting Zhang ◽  
Kun Zhang ◽  
Xun Li ◽  
Laishui Lv ◽  
Qi Sun
Keyword(s):  
Matrix Factorization ◽  
Link Prediction ◽  
Temporal Networks ◽  
Non Negative Matrix Factorization

scholarly journals Diamond Subgraphs in the Reduction Graph of a One-Rule String Rewriting System

2021 ◽  
Vol 178 (3) ◽  
pp. 173-185
Author(s):  
Arthur Adinayev ◽  
Itamar Stein
Keyword(s):  
Isomorphism Problem ◽  
Complete Characterization ◽  
Hasse Diagram ◽  
Subgraph Isomorphism ◽  
Rewriting Systems ◽  
Rewriting System ◽  
String Rewriting ◽  
Reduction Graph

In this paper, we study a certain case of a subgraph isomorphism problem. We consider the Hasse diagram of the lattice Mk (the unique lattice with k + 2 elements and one anti-chain of length k) and find the maximal k for which it is isomorphic to a subgraph of the reduction graph of a given one-rule string rewriting system. We obtain a complete characterization for this problem and show that there is a dichotomy. There are one-rule string rewriting systems for which the maximal such k is 2 and there are cases where there is no maximum. No other intermediate option is possible.

Seasonal-Periodic Subgraph Mining in Temporal Networks

2020 ◽  
Author(s):  
Qianzhen Zhang ◽  
Deke Guo ◽  
Xiang Zhao ◽  
Xinyi Li ◽  
Xi Wang
Keyword(s):  
Temporal Networks ◽  
Subgraph Mining

Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation

2021 ◽  
pp. 115471
Author(s):  
Laishui Lv ◽  
Kun Zhang ◽  
Ting Zhang ◽  
Xun Li ◽  
Qi Sun ◽  
...  
Keyword(s):  
Temporal Networks
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