In this chapter, we propose the cluster-based graph collaborative filtering model, which performs high-order graph convolution over cluster-specific graphs. This approach enhances the model's ability to filter out negative information while extracting valuable information from high-order neighboring nodes.Liu, FanNational University of SingaporeNie, Liq...
In addition, we propose a collaborative filtering based approach, in which top-k similar users are filtered by resource-interest-based profiles; resource similarities are obtained by tag-frequency-based profiles; the candidate resources are then ranked according to the user interest model, resource ...
The graph in Fig. 11 clearly shows that the percolation of the target increases as the density of the network (number of sensor nodes) increases, which is quite natural given the very definition of percolation. On the other hand, the percolation rate is much better for low values of the ...
Pregel [21] is a programming model for graph applications based on the Bulk Synchronous Parallel paradigm [32]. Programs run as a series of coordinated iterations called supersteps. On each superstep, each vertex in the graph runs a user function that can update state associated with the vertex...
(T. cruziorT. brucei) and the equivalent syntenic regions in the other genomes. For clarity, in this figure only the first three genes for down and up-stream regions are shown. Homologous genes are vertically aligned. The graph shows when the syntenic genes are observed using both Simple...
cluster ensembles ‘knowledge reuse’ framework cluster ensembles in application scenarios graph‐theoretic approaches (CSPA, HGPA, and MCLA) cluster‐based similarity partitioning algorithm (CSPA) soft cluster ensembles Information㏕heoretic (ITK) based approach normalized mutual information (NMI) soft en...
On the practical side, we designed a data filtering-based distributed geometric fusion positioning method for heterogeneous navigation information. This method constructs a factor graph architecture that can process multi-source data in real-time, significantly enhancing the positioning stability and accuracy...
Advances in Big Data Programming System Software and HPC Convergence; Advances in Scalable Computing Techniques; Sustainable Clusters and Cloud Computing; Advances in Multimedia-based Cloud-Computing for Healthcare; AI techniques for cluster computing and sensor networks; Collaborative Processing of Big Data...
GNNs are crafted to account for the graph’s structure, enabling the creation of efficient embeddings at both graph and node levels, exemplified in applications like graph-based malware classification. In the context of a GNN malware classifier, node-level features are consolidated to produce graph...
20070239554Cluster-based scalable collaborative filtering2007-10-11Lin705/26.7 20050105775Method of using temporal context for image classification2005-05-19Luo et al. 20050089878Method for determining functional sites in a protein2005-04-28Debe435/6.14 ...