From clustering to clustering ensemble selection: A review
Clustering, as an unsupervised learning, is aimed at discovering the natural grou**s of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
A review of clustering techniques and developments
This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …
developments made at various times. Clustering is defined as an unsupervised learning …
Graph representation learning: a survey
Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …
since most data in real-world applications come in the form of graphs. High-dimensional …
{PowerGraph}: Distributed {Graph-Parallel} computation on natural graphs
Large-scale graph-structured computation is central to tasks ranging from targeted
advertising to natural language processing and has led to the development of several graph …
advertising to natural language processing and has led to the development of several graph …
Community detection in networks with node attributes
Community detection algorithms are fundamental tools that allow us to uncover
organizational principles in networks. When detecting communities, there are two possible …
organizational principles in networks. When detecting communities, there are two possible …
Distributed graphlab: A framework for machine learning in the cloud
Y Low, J Gonzalez, A Kyrola, D Bickson… - ar** community detection at scale: a nonnegative matrix factorization approach
Network communities represent basic structures for understanding the organization of real-
world networks. A community (also referred to as a module or a cluster) is typically thought of …
world networks. A community (also referred to as a module or a cluster) is typically thought of …
{GraphX}: Graph processing in a distributed dataflow framework
In pursuit of graph processing performance, the systems community has largely abandoned
general-purpose distributed dataflow frameworks in favor of specialized graph processing …
general-purpose distributed dataflow frameworks in favor of specialized graph processing …
Federated graph neural networks: Overview, techniques, and challenges
Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …
due to their capability to progress with graph data and have been widely used in practical …