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Volodymyr Miz

dblp:137/9149 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 91% Web and social media mining · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
anomaly detection		 0.412019
Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory · WWW 2019
Data mining › structured data mining › graph mining › dynamic network analysis
dynamic graph mining		 0.412019
Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory · WWW 2019
Data mining › structured data mining
graph mining		 0.412019
Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory · WWW 2019
Web and social media mining
social network analysis		 0.112019
Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory · WWW 2019

Methods — techniques the papers use, named apart from their topics

unsupervised learning · 0.4associative memory · 0.4
YearPublicationVenuePosition
2019 Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory
abstract
In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources.
Volodymyr Miz, Benjamin Ricaud, Kirell Benzi, Pierre Vandergheynst
 WWW1