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TGB 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs
Gastinger, Julia
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Huang, Shenyang
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Galkin, Mikhail
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Loghmani, Erfan
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Parviz, Ali
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Poursafaei, Farimah
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Danovitch, Jacob
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Rossi, Emanuele
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Koutis, Ioannis
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Stuckenschmidt, Heiner
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Rabbany, Reihaneh
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Rabusseau, Guillaume
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URL:
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https://papers.nips.cc/paper_files/paper/2024/hash...
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Additional URL:
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https://openreview.net/forum?id=EADRzNJFn1#discuss...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2024
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Book title:
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Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track
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Page range:
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140199-140229
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Conference title:
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NeurIPS 2024
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Location of the conference venue:
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Vancouver, Canada
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Date of the conference:
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09.-15.12.2024
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Publisher:
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Globerson, A.
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Mackey, L.
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Belgrave, D.
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Fan, A.
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Paquet, U.
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Tomczak, J.
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Zhang, C.
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Place of publication:
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Vancouver, BC
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Publishing house:
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Curran Associates, Inc.
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Subject:
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004 Computer science, internet
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Abstract:
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Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly largerthan existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Gastinger, Julia
;
Huang, Shenyang
;
Galkin, Mikhail
;
Loghmani, Erfan
;
Parviz, Ali
;
Poursafaei, Farimah
;
Danovitch, Jacob
;
Rossi, Emanuele
;
Koutis, Ioannis
;
Stuckenschmidt, Heiner
;
Rabbany, Reihaneh
;
Rabusseau, Guillaume
Google Scholar:
Gastinger, Julia
;
Huang, Shenyang
;
Galkin, Mikhail
;
Loghmani, Erfan
;
Parviz, Ali
;
Poursafaei, Farimah
;
Danovitch, Jacob
;
Rossi, Emanuele
;
Koutis, Ioannis
;
Stuckenschmidt, Heiner
;
Rabbany, Reihaneh
;
Rabusseau, Guillaume
ORCID:
Gastinger, Julia ; Huang, Shenyang ; Galkin, Mikhail ; Loghmani, Erfan ; Parviz, Ali ; Poursafaei, Farimah ; Danovitch, Jacob ; Rossi, Emanuele ; Koutis, Ioannis ; Stuckenschmidt, Heiner  ORCID: 0000-0002-0209-3859 ; Rabbany, Reihaneh ; Rabusseau, Guillaume
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