The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
Twitter and research: A systematic literature review through text mining
Researchers have collected Twitter data to study a wide range of topics. This growing body
of literature, however, has not yet been reviewed systematically to synthesize Twitter-related …
of literature, however, has not yet been reviewed systematically to synthesize Twitter-related …
Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction
Abstract Graph Neural Networks (GNNs) have been widely applied to various fields for
learning over graph-structured data. They have shown significant improvements over …
learning over graph-structured data. They have shown significant improvements over …
The four dimensions of social network analysis: An overview of research methods, applications, and software tools
Social network based applications have experienced exponential growth in recent years.
One of the reasons for this rise is that this application domain offers a particularly fertile …
One of the reasons for this rise is that this application domain offers a particularly fertile …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
GraphP: Reducing communication for PIM-based graph processing with efficient data partition
Processing-In-Memory (PIM) is an effective technique that reduces data movements by
integrating processing units within memory. The recent advance of “big data” and 3D …
integrating processing units within memory. The recent advance of “big data” and 3D …
Community detection in social media: Performance and application considerations
The proposed survey discusses the topic of community detection in the context of Social
Media. Community detection constitutes a significant tool for the analysis of complex …
Media. Community detection constitutes a significant tool for the analysis of complex …
Graphq: Scalable pim-based graph processing
Processing-In-Memory (PIM) architectures based on recent technology advances (eg,
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …
Dense subgraph extraction with application to community detection
This paper presents a method for identifying a set of dense subgraphs of a given sparse
graph. Within the main applications of this “dense subgraph problem,” the dense subgraphs …
graph. Within the main applications of this “dense subgraph problem,” the dense subgraphs …
Community detection with edge content in social media networks
The problem of community detection in social media has been widely studied in the social
networking community in the context of the structure of the underlying graphs. Most …
networking community in the context of the structure of the underlying graphs. Most …