Various graphs such as web or social networks may contain up to trillions of edges.
Compressing such datasets can accelerate graph processing by reducing the amount of I/O …

(57) ABSTRACT A learning agent is disclosed that receives data in sequence from one or
more sequential data sources, generates a model modelling sequences of data and actions …

Recently, network embedding that encodes structural information of graphs into a vector
space has become popular for network analysis. Although recent methods show promising …

Comprehensive evaluation is one of the basis of experimental science. In High-Performance
Graph Processing, a thorough evaluation of contributions becomes more achievable by …

We consider the problem of graph data compression and representation. Recent
developments in graph neural networks (GNNs) focus on generalizing convolutional neural …

In this paper, I sketch a new model for the format of the content of understanding states,
Compressible Graph Maximalism (CGM). In this model, the format of the content of …

Graphs are used in diverse set of disciplines ranging from computer networks to biological
networks, social networks, World Wide Web etc. With the advancement in the technology …

In this paper, I sketch a new model for the format of the content of understanding states,
Compressible Graph Maximalism (CGM). In this model, the format of the content of …

This doctoral thesis will present the results of my work into querying graphs on large-scale
data, from both the data perspective and query perspective. We first propose a scheme to …

Les problèmes MCE (Maximal Clique Enumeration) et MCP (Maximum Clique Problem)
sont des problèmes que l'on rencontre dans l'analyse des graphes de données et leur …