Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Jacob Chmura

dblp:299/4138 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021

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.

Artificial intelligence
1 paper
Graph learning · 100%
Databases, data mining, and information retrieval
1 paper
Recommender systems · 50% Web and social media mining · 50%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning
dynamic graph learning		 0.912025
Temporal Graph Learning Workshop · KDD (2) 2025
Web and social media mining
social network analysis		 0.312025
Temporal Graph Learning Workshop · KDD (2) 2025
Recommender systems › context-aware recommendation › dynamic recommendation
temporal recommendation		 0.312025
Temporal Graph Learning Workshop · KDD (2) 2025

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

temporal graph learning · 1.7graph neural network · 1.7
YearPublicationVenuePosition
2025 Temporal Graph Learning Workshop
abstract
The Temporal Graph Learning (TGL) workshop, now in its third edition at KDD 2025, offers an interdisciplinary platform for researchers to explore the evolving applications of temporal networks in various domains, including recommender systems, social network analysis, traffic analytics, and epidemiological data analysis.The workshop aims to facilitate the exchange of ideas across disciplines, highlight successes and challenges in TGL, and outline future research directions.The workshop welcomes diverse contributions, offers keynote talks from academic and industry experts, and is complemented by a panel discussion on emerging aspects of TGL.
Shenyang Huang, Daniele Zambon, Andrea Cini, Farimah Poursafaei, Jacob Chmura, Julia Gastinger, Reihaneh Rabbany, Michael M. Bronstein
 KDD (2)5