Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
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 …
FCAN-MOPSO: an improved fuzzy-based graph clustering algorithm for complex networks with multiobjective particle swarm optimization
L Hu, Y Yang, Z Tang, Y He… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Performing an accurate clustering analysis is of great significance for us to understand the
behavior of complex networks, and a variety of graph clustering algorithms have, thus, been …
behavior of complex networks, and a variety of graph clustering algorithms have, thus, been …
Effective hierarchical clustering based on structural similarities in nearest neighbor graphs
C Wu, Q Peng, J Lee, K Leibnitz, Y ** heterogeneous and non-
spherical datasets than the center-based clustering, at the expense of increased time …
spherical datasets than the center-based clustering, at the expense of increased time …
[KNIHA][B] Community search over big graphs
Communities serve as basic structural building blocks for understanding the organization of
many real-world networks, including social, biological, collaboration, and communication …
many real-world networks, including social, biological, collaboration, and communication …
EGC: A novel event-oriented graph clustering framework for social media text
With the popularity of social platforms such as Sina Weibo, Tweet, etc., a large number of
public events spread rapidly on social networks and huge amount of textual data are …
public events spread rapidly on social networks and huge amount of textual data are …
An optimal and progressive approach to online search of top-k influential communities
Community search over large graphs is a fundamental problem in graph analysis. Recent
studies propose to compute top-k influential communities, where each reported community …
studies propose to compute top-k influential communities, where each reported community …
Activelink: deep active learning for link prediction in knowledge graphs
Neural networks have recently been shown to be highly effective at predicting links for
constructing knowledge graphs. Existing research has mainly focused on designing 1) deep …
constructing knowledge graphs. Existing research has mainly focused on designing 1) deep …
Coronavirus pandemic analysis through tripartite graph clustering in online social networks
The COVID-19 pandemic has hit the world hard. The reaction to the pandemic related issues
has been pouring into social platforms, such as Twitter. Many public officials and …
has been pouring into social platforms, such as Twitter. Many public officials and …
Identifying similar-bicliques in bipartite graphs
Bipartite graphs have been widely used to model the relationship between entities of
different types, where vertices are partitioned into two disjoint sets/sides. Finding dense …
different types, where vertices are partitioned into two disjoint sets/sides. Finding dense …