
Efficient Scaling of Dynamic Graph Neural Networks
We present distributed algorithms for training dynamic Graph Neural Netw...
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Finding All BoundedLength Simple Cycles in a Directed Graph
A new efficient algorithm is presented for finding all simple cycles tha...
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Heterogeneous Graph Neural Networks for Multilabel Text Classification
Multilabel text classification (MLTC) is an attractive and challenging ...
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TimeEfficient and HighQuality Graph Partitioning for Graph Dynamic Scaling
The dynamic scaling of distributed computations plays an important role ...
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The Impact of COVID19 on Flight Networks
As COVID19 transmissions spread worldwide, governments have announced a...
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Benchmarking Graph Data Management and Processing Systems: A Survey
The development of scalable, representative, and widely adopted benchmar...
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Exploring MultiBanking CustomertoCustomer Relations in AML Context with Poincaré Embeddings
In the recent years money laundering schemes have grown in complexity an...
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Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis
Recently, there has been a surge of interest in the use of machine learn...
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Towards Federated Graph Learning for Collaborative Financial Crimes Detection
Financial crime is a large and growing problem, in some way touching alm...
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Distributed Edge Partitioning for Trillionedge Graphs
We propose Distributed Neighbor Expansion (Distributed NE), a parallel a...
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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Graph representation learning resurges as a trending research subject ow...
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Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
Graph pattern matching algorithms to handle millionscale dynamic graphs...
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Scalable Graph Learning for AntiMoney Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexic...
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An incremental localfirst community detection method for dynamic graphs
Community detections for largescale real world networks have been more ...
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Scalable attributeaware network embedding with locality
Adding attributes for nodes to network embedding helps to improve the ab...
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Scalable attributeaware network embedding with localily
Adding attributes for nodes to network embedding helps to improve the ab...
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System G Distributed Graph Database
Motivated by the need to extract knowledge and value from interconnected...
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FullNetwork Embedding in a Multimodal Embedding Pipeline
The current stateoftheart for image annotation and image retrieval ta...
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Building Graph Representations of Deep Vector Embeddings
Patterns stored within pretrained deep neural networks compose large an...
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Can GAN Learn Topological Features of a Graph?
This paper is firstline research expanding GANs into graph topology ana...
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An Outofthebox Fullnetwork Embedding for Convolutional Neural Networks
Transfer learning for feature extraction can be used to exploit deep rep...
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Medical Text Classification using Convolutional Neural Networks
We present an approach to automatically classify clinical text at a sent...
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On the Behavior of Convolutional Nets for Feature Extraction
Deep neural networks are representation learning techniques. During trai...
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Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
We present a scalable parallel solver for numerical constraint satisfact...
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Scalable Parallel Numerical CSP Solver
We present a parallel solver for numerical constraint satisfaction probl...
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Toyotaro Suzumura
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