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SPAN: Subgraph Prediction Attention Network for Dynamic Graphs [article]

Yuan Li, Chuanchang Chen, Yubo Tao, Hai Lin
2021 arXiv   pre-print
This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction.  ...  We compare our model with several state-of-the-art methods for subgraph prediction and subgraph pattern prediction in multiple real-world homogeneous and heterogeneous dynamic graphs, respectively.  ...  Bayesian subgraph sampling is responsible for generating subgraphs, and SPAN model is used for the subgraph prediction of dynamic graphs.  ... 
arXiv:2108.07776v1 fatcat:s34vnwv7yvhkbnylsevsfccoja

Span-core Decomposition for Temporal Networks: Algorithms and Applications [article]

Edoardo Galimberti, Martino Ciaperoni, Alain Barrat, Francesco Bonchi, Ciro Cattuto, Francesco Gullo
2020 arXiv   pre-print
For a temporal network defined on a discrete temporal domain T, the total number of time intervals included in T is quadratic in |T|, so that the total number of span-cores is potentially quadratic in  ...  the period of time for which the high density holds).  ...  [29] deal with the problem of maintaining the densest subgraph in a dynamic setting. Attention in the literature has also been devoted to densities other than the average degree.  ... 
arXiv:1910.03645v2 fatcat:trhxhbnhcrc5hffi7wjx2humde

Spanders: Distributed spanning expanders

Shlomi Dolev, Nir Tzachar
2013 Science of Computer Programming  
. • We then employ our results to construct a hierarchical sequence of spanders, each of them an expander spanning the previous graph.  ...  Given an expander graph, G, a spander, S, is a subgraph of G such that S is an expander using a subset of the edges of G. • First we consider the case in which the communication graph is a complete graph  ...  It is a pleasure to thank Eitan Bachmat and Noga Alon for helpful discussions. In particular, some of the techniques were explored during and after discussions with Noga.  ... 
doi:10.1016/j.scico.2012.10.001 fatcat:siaesihl5za2vkuuwnhpxaueii

A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition [article]

Fei Li, Zhichao Lin, Meishan Zhang, Donghong Ji
2021 arXiv   pre-print
Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.  ...  Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities.  ...  Acknowledgments We thank the reviewers for their comments and recommendation. This work is supported by the National Natural Science  ... 
arXiv:2106.14373v1 fatcat:lyyvifwsjzgbhcv4nct75xqm6m

Cluster Counting: The Hoshen–Kopelman Algorithm Versus Spanning Tree Approaches

F. Babalievski
1998 International Journal of Modern Physics C  
The graph-theoretical basis for the spanning tree approaches is given by describing the "breadth-first search" and "depth-first search" procedures.  ...  The Hoshen-Kopelman multiple labeling technique for cluster statistics is redescribed.  ...  MacNamara for the critical reading of the manuscript. This work was supported partially by a grant from the German Academic Exchange Foundation (DAAD).  ... 
doi:10.1142/s0129183198000054 fatcat:rxi6pyqxybg7tcefwl6pggdxk4

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Min Dong, Xuhang Zhang, Kun Yang, Rui Liu, Pei Chen
2021 PeerJ  
Methods By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM  ...  Results The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level.  ...  Yingqi Chen for productive discussions.  ... 
doi:10.7717/peerj.11603 pmid:34249495 pmcid:PMC8253113 fatcat:chifwwbjgffq7nc26ipjtf7r4e

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

Chunlei Shi, Jiacai Zhang, Xia Wu
2020 Symmetry  
Here, we proposed a novel feature selection method based on the minimum spanning tree (MST) to seek neuromarkers for ASD. First, we constructed an undirected graph with nodes of candidate features.  ...  Second, we utilized the Prim algorithm to construct the MST from the initial graph structure. Third, the sum of the edge weights of all connected nodes was sorted for each node in the MST.  ...  The time complexity of this algorithm is O(n 2 ), which is independent of the number of edges in the graph and is suitable for the minimum spanning tree calculation of dense graphs.  ... 
doi:10.3390/sym12121995 fatcat:wyccgsil4vh3he2ryskvuxbl64

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Xiaowei Li, Zhuang Jing, Bin Hu, Jing Zhu, Ning Zhong, Mi Li, Zhijie Ding, Jing Yang, Lan Zhang, Lei Feng, Dennis Majoe
2017 Complexity  
Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear.  ...  Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study.  ...  The minimum spanning tree is a simple acyclic connected subgraph of the original weighted network that can be used to direct comparison of networks with the same number of nodes and simplifies the network  ... 
doi:10.1155/2017/9514369 fatcat:btackncmt5bqfmrqwjfhejelcq

Brain Network Constraints and Recurrent Neural Networks reproduce unique Trajectories and State Transitions seen over the span of minutes in resting state fMRI

Amrit Kashyap, Shella Keilholz
2020 Network Neuroscience  
The manuscript demonstrates that by using Recurrent Neural Networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data  ...  Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent timepoints, and can simulate complex resting state trajectories better than the traditional generative  ...  Christopher Rozell for his insightful discussion on the interpretation of the autoencoder. AUTHOR CONTRIBUTIONS  ... 
doi:10.1162/netn_a_00129 pmid:32537536 pmcid:PMC7286308 fatcat:gkl5smssqratzoxjo36u46k7iu

Pre-Training on Dynamic Graph Neural Networks [article]

Ke-jia Chen, Jiajun Zhang, Linpu Jiang, Yunyun Wang, Yuxuan Dai
2022 arXiv   pre-print
Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task.  ...  This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution  ...  For link prediction task, a loss function L F based on dynamic graph is defined.  ... 
arXiv:2102.12380v2 fatcat:4wcmhk2vfng5dehdkuxmlxae7m

Expanding Network Analysis Tools in Psychological Networks: Minimal Spanning Trees, Participation Coefficients, and Motif Analysis Applied to a Network of 26 Psychological Attributes

Srebrenka Letina, Tessa F. Blanken, Marie K. Deserno, Denny Borsboom
2019 Complexity  
We explore the potential value of minimum spanning trees, participation coefficients, and motif analyses and demonstrate the relevant analyses using a network of 26 psychological attributes.  ...  The analysis of psychological networks in previous research has been limited to the inspection of centrality measures and the quantification of specific global network features.  ...  We thank Donald Williams for the help in the estimation of nonregularized partial correlation network and Tamer Khraisha for advice on coding and visualizations.  ... 
doi:10.1155/2019/9424605 fatcat:f6uszsvdbzcgzaxrdhqk472f6q

Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast [article]

Linpu Jiang, Ke-Jia Chen, Jingqiang Chen
2021 arXiv   pre-print
features of a dynamic graph.  ...  Self-supervised learning on graphs has recently drawn a lot of attention due to its independence from labels and its robustness in representation.  ...  The method of using graph neural networks (GNN) [10, 26, 33, 36] has recently drawn considerable attention and achieved excellent performance.  ... 
arXiv:2112.08733v1 fatcat:s36pwbona5gs7pcfyzvcj5hw2u

Research of NP-Complete Problems in the Class of Prefractal Graphs

Rasul Kochkarov
2021 Mathematics  
NP-complete problems in graphs, such as enumeration and the selection of subgraphs with given characteristics, become especially relevant for large graphs and networks.  ...  We propose a class of prefractal graphs and review particular statements of NP-complete problems. As an example, algorithms for searching for spanning trees and packing bipartite graphs are proposed.  ...  Thus, we propose to use all the available tools of dynamic prefractal graphs to solve optimization problems in large networks, as well as to design networks with given characteristics, including predicting  ... 
doi:10.3390/math9212764 fatcat:kitwgsxezvdhfahfz5lhyrt2ca

Graph Neural Networks Including Sparse Interpretability [article]

Chris Lin, Gerald J. Sun, Krishna C. Bulusu, Jonathan R. Dry, Marylens Hernandez
2020 arXiv   pre-print
With any GNN model, GISST combines an attention mechanism and sparsity regularization to yield an important subgraph and node feature subset related to any graph-based task.  ...  Here we present a model-agnostic framework for interpreting important graph structure and node features, Graph neural networks Including SparSe inTerpretability (GISST).  ...  Acknowledgements We thank the entire Early Computational Oncology group at AstraZeneca, especially the Knowledge Graphs, Data Science, and Bioinformatics working groups, for their invaluable feedback and  ... 
arXiv:2007.00119v1 fatcat:3xxsj6qldzftlneh6f4ioly3ei

Mining Periodic Behavior in Dynamic Social Networks

Mayank Lahiri, Tanya Y. Berger-Wolf
2008 2008 Eighth IEEE International Conference on Data Mining  
To identify such regular behavior, we propose a new mining problem of finding periodic or near periodic subgraphs in dynamic social networks.  ...  We demonstrate the applicability of our approach on several real-world networks and extract meaningful and interesting periodic interaction patterns.  ...  We thank Kapil Thadani for valuable discussions. This work is supported by NSF grants IIS-0705822 and CAREER IIS-0747369.  ... 
doi:10.1109/icdm.2008.104 dblp:conf/icdm/LahiriB08 fatcat:smtp7e7aajfvrmechbaf2gf5ky
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