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Ordered Subgraph Aggregation Networks
[article]
2022
arXiv
pre-print
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non-data-driven subgraph-enhanced graph neural networks while reducing computation time. ...
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs. ...
Essentially, the k-OSWL labels or marks ordered subgraphs and then executes 1-WL on top of the marked graphs, followed by an aggregation phase. ...
arXiv:2206.11168v2
fatcat:y7y5glkspzdclopkgw3ikrk4je
Robust android malware detection based on subgraph network and denoising GCN network
2022
Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services
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The model uses the subgraph network to detect the underlying structural features of the FCG and discover the confusion attack. ...
A denoising graph neural network is applied to graph convolution to reduce the impact of obfuscation attacks. ...
For second-order SGNs, open triangles (with at least 2 edges) are defined as the basic subgraph units for building second-order subgraph networks, SGN (2) . ...
doi:10.1145/3498361.3538778
fatcat:remvqssjjnajrerporfygjnaey
HoNVis: Visualizing and Exploring Higher-Order Networks
[article]
2017
arXiv
pre-print
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Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. ...
In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. ...
subgraph and the entire network. ...
arXiv:1702.00737v1
fatcat:2rl3yxp3tzhele6kwfw3sjin6u
MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
2021
Applied Sciences
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Finally, the node representations are obtained by aggregating the representation vectors of nodes in each subgraph. ...
So we proposed a new method called Multi-Subgraph based Graph Convolution Network (MSGCN), which uses topology information, semantic information, and node feature information to learn node embedding vector ...
In order to address the above problems, this paper proposes a new model to deal with heterogeneous graphs, called MSGCN (Multi-Subgraph based Graph Convolution Network). ...
doi:10.3390/app11219832
fatcat:jllatfdxlzdvjd5sqeejuy6twe
Parallel discovery of network motifs
2012
Journal of Parallel and Distributed Computing
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Many natural structures can be naturally represented by complex networks. ...
Our strategies are capable of dealing both with exact and approximate network motif discovery. ...
This is a huge a amount of data that we need to aggregate in order to calculate the subgraph significance. Note that the number of possible k-subgraphs grows super-exponentially as k increases. ...
doi:10.1016/j.jpdc.2011.08.007
fatcat:cot76i6rpbaejb2yqnl2243vem
The topological relationship between the large-scale attributes and local interaction patterns of complex networks
2004
Proceedings of the National Academy of Sciences of the United States of America
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but must naturally aggregate into subgraph clusters. ...
networks. ...
but must naturally aggregate into subgraph clusters. ...
doi:10.1073/pnas.0406024101
pmid:15598746
pmcid:PMC539752
fatcat:xmxnkzwkjreszdlmpo4dbka4fy
Atomic subgraphs and the statistical mechanics of networks
[article]
2020
arXiv
pre-print
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These models include random graphs with arbitrary distributions of subgraphs, random hypergraphs, bipartite models, stochastic block models, models of multilayer networks and their degree corrected and ...
This allows the for the generation of graphs with extensive numbers of triangles and other network motifs commonly observed in many real world networks. ...
Higher order atoms and network communities Including higher order structures such as triangles in generative models can have a significant impact on inference of network communities. ...
arXiv:2008.10346v1
fatcat:ti6g26ouxzawxmd5ojcbutiaxe
Neural Subgraph Matching
[article]
2020
arXiv
pre-print
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NeuroMatch decomposes query and target graphs into small subgraphs and embeds them using graph neural networks. ...
Despite being an NP-complete problem, the subgraph matching problem is crucial in domains ranging from network science and database systems to biochemistry and cognitive science. ...
NeuroMatch trains a graph neural network to learn the order embedding, and uses a max-margin loss to ensure that the subgraph relationships are captured. ...
arXiv:2007.03092v2
fatcat:iukf3larbbdt5mgyblw3lca5cq
Maximal prime subgraph decomposition of Bayesian networks
2002
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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In this paper we present a method for decomposition of Bayesian networks into their maximal prime subgraphs. ...
We also identify a number of tasks performed on Bayesian networks that can benefit from maximal prime subgraph decomposition. ...
Further research is needed in order to draw precise conclusions on the importance of exploiting maximal prime subgraph decompositions. ...
doi:10.1109/3477.979956
pmid:18238100
fatcat:vg4qovtxszbshcnu2lwrq33ikq
Robust Hierarchical Graph Classification with Subgraph Attention
[article]
2020
arXiv
pre-print
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Attention mechanism applied on the neighborhood of a node improves the performance of graph neural networks. ...
Graph neural networks get significant attention for graph representation and classification in machine learning community. ...
Besides, recent literature also shows that higher order GNNs that directly aggregate features from higher order neighborhood of a node are theoretically more powerful than 1st order GNNs [24] . ...
arXiv:2007.10908v1
fatcat:h6jbr6ke5jclhpofwvy64k6sey
VCExplorer: A Interactive Graph Exploration Framework Based on Hub Vertices with Graph Consolidation
[article]
2017
arXiv
pre-print
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In addition, we propose efficient graph aggregation algorithms over multiple subgraphs via computation sharing. ...
Graphs have been widely used to model different information networks, such as the Web, biological networks and social networks (e.g. Twitter). ...
INTRODUCTION Graphs are powerful tools to model a variety of information networks, such as the Web, biological networks and social networks (e.g. Twitter). ...
arXiv:1709.06745v1
fatcat:z6nvkc7xnffipf4cax3dutydqm
A Multi-Scale Approach for Graph Link Prediction
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. ...
Graphs in different scales can provide scale-invariant information, which enables graph neural networks to learn invariant features and improve link prediction performance. ...
By aggregating the subgraph iteratively, we can obtain a series of subgraphs in different scales. ...
doi:10.1609/aaai.v34i04.5731
fatcat:7zgj6kjmxzahvjz724k2fu2u7m
A Self-adaptive Fault-Tolerant Mechanism in Wireless Sensor Networks
[chapter]
2009
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Moreover, we discussed and tried to solve the problems on FTMAWSS (fault-tolerant maximum average-weighted spanning subgraph) in weighted connected graph initiating from clustering WSNs. ...
It is crucial for the sensors to form an optimal network topology and tune to transmission attempt rates in a way that optimize network throughput. ...
So the self-adaptive of sensor networks can be used to construct an optimal network subgraph with fault tolerant in this paper in order to form an effective, practical subgraph topology. ...
doi:10.1007/978-3-642-10485-5_17
fatcat:hcgexo7uqfdmrm3boafsdwyrmy
Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
[article]
2022
arXiv
pre-print
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2021
Proceedings of the Web Conference 2021
unpublished
doi:10.1145/3442381.3449929 fatcat:orwh2m4llff3zd3ip3yrx7ngnu
doi:10.1145/3442381.3449929 fatcat:orwh2m4llff3zd3ip3yrx7ngnu
A graph neural network should be able to efficiently extract task-relevant structures and be invariant to irrelevant parts, which is challenging for general message passing GNNs. ...
more flexible to extract subgraphs with arbitrary sizes. ...
The soft-mask GNN layer applies continuous mask values in order to maintain the differentiability of the networks. ...
arXiv:2206.05499v1
fatcat:kzv2bi3qwnby7a4pwscpqtsysy
Metapath- and Entity-aware Graph Neural Network for Recommendation
[article]
2021
arXiv
pre-print
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We propose metaPath and Entity-Aware Graph Neural Network (PEAGNN), which trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs. ...
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. ...
NFM [14] utilizes neural networks to enhance high-order feature interactions with non-linearity. As suggested by He et al. [14] , we apply one hidden layer neural network on input features. 2. ...
arXiv:2010.11793v3
fatcat:xn6j6yhyevdcdmssxpgmo4cwcq
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