Dynamic graphEdge influenceReachability queries are of great importance in many research and application areas, including general graph mining, social network analysis and so on. Many approaches have been propo
Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks,...
Network Embedding as Matrix Factorization - Unifying DeepWalk, LINE, PTE, and node2vec Neighbor Interaction Aware Graph Convolution Networks for Recommendation OmniSage - Large Scale, Multi-Entity Heterogeneous Graph Representation Learning Package Recommendation with Intra- and Inter-Package Attention Network...
Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed...
2.1. Network Model of Emotion Processing The model is divided into three layers: the cloud control center layer; the edge node layer; and the user layer [12–14]. In the cloud control center layer, all music emotional data are uploaded to cloud computing for data storage and information emo...
A physically motivated model for neuromorphic structure and function in nanowire networks PVP-coated Ag nanowires self-assemble to form a highly disordered, complex network topology (experiment: Fig.1a; simulation: Supplementary Fig.1). As a neuromorphic device, NWNs are operated by applying an elec...
Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model...
The core of a GNN algorithm is recursive neighborhood aggregation, in which each node aggregates the feature vectors of its multi-hop neighbors to calculate its new feature vector. In a large and dense graph, this aggregation phase requires much computation, which means that the training and ...
Based on the traditional recommendation technology, a group recommendation system for network document resource discovery based on the knowledge graph and LSTM in edge computing is proposed, which is able to work out the target information in accordance with users’ needs proactively and solve the “...
At the same time, the contemporary smart grid requires to realize the cooperation across power areas and voltage levels, and monitor the real-time status of each node of the power grid. However, there are a large number of nodes in the power system. How to process the massive data ...