Revealing and analyzing the various properties of materials is an essent...
The prediction of material properties plays a crucial role in the develo...
Precisely recommending candidate news articles to users has always been ...
Lookalike models are based on the assumption that user similarity plays ...
Knowledge graph completion (KGC) aims to discover missing relations of q...
In this paper we present a novel method, Knowledge Persistence
(𝒦𝒫), for...
Spatio-temporal modeling as a canonical task of multivariate time series...
Traffic forecasting as a canonical task of multivariate time series
fore...
Learning on evolving(dynamic) graphs has caught the attention of researc...
Leveraging graphs on recommender systems has gained popularity with the
...
The growing amount of data and advances in data science have created a n...
While transactions with cryptocurrencies such as Ethereum are becoming m...
Transformers have been proven to be inadequate for graph representation
...
We present distributed algorithms for training dynamic Graph Neural Netw...
A new efficient algorithm is presented for finding all simple cycles tha...
Multi-label text classification (MLTC) is an attractive and challenging ...
The dynamic scaling of distributed computations plays an important role ...
As COVID-19 transmissions spread worldwide, governments have announced a...
The development of scalable, representative, and widely adopted benchmar...
In the recent years money laundering schemes have grown in complexity an...
Recently, there has been a surge of interest in the use of machine learn...
Financial crime is a large and growing problem, in some way touching alm...
We propose Distributed Neighbor Expansion (Distributed NE), a parallel a...
Graph representation learning resurges as a trending research subject ow...
Graph pattern matching algorithms to handle million-scale dynamic graphs...
Organized crime inflicts human suffering on a genocidal scale: the Mexic...
Community detections for large-scale real world networks have been more
...
Adding attributes for nodes to network embedding helps to improve the ab...
Adding attributes for nodes to network embedding helps to improve the ab...
Motivated by the need to extract knowledge and value from interconnected...
The current state-of-the-art for image annotation and image retrieval ta...
Patterns stored within pre-trained deep neural networks compose large an...
This paper is first-line research expanding GANs into graph topology
ana...
Transfer learning for feature extraction can be used to exploit deep
rep...
We present an approach to automatically classify clinical text at a sent...
Deep neural networks are representation learning techniques. During trai...
We present a scalable parallel solver for numerical constraint satisfact...
We present a parallel solver for numerical constraint satisfaction probl...