
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Inferring missing facts in temporal knowledge graphs (TKGs) is a fundame...
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Directional Graph Networks
In order to overcome the expressive limitations of graph neural networks...
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Structure Aware Negative Sampling in Knowledge Graphs
Learning lowdimensional representations for entities and relations in k...
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VeRNAl: A Tool for Mining Fuzzy Network Motifs in RNA
Motivation: RNAs are ubiquitous molecules involved in many regulatory an...
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Adversarial Example Games
The existence of adversarial examples capable of fooling trained neural ...
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Learning an Unreferenced Metric for Online Dialogue Evaluation
Evaluating the quality of a dialogue interaction between two agents is a...
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Evaluating Logical Generalization in Graph Neural Networks
Recent research has highlighted the role of relational inductive biases ...
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Learning Dynamic Knowledge Graphs to Generalize on TextBased Games
Playing textbased games requires skill in processing natural language a...
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Latent Variable Modelling with Hyperbolic Normalizing Flows
The choice of approximate posterior distributions plays a central role i...
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MetaGraph: Few shot Link Prediction via Meta Learning
Fast adaptation to new data is one key facet of human intelligence and i...
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Inductive Relation Prediction on Knowledge Graphs
Inferring missing edges in multirelational knowledge graphs is a fundam...
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Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
The recent success of natural language understanding (NLU) systems has b...
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Neural Transfer Learning for Crybased Diagnosis of Perinatal Asphyxia
Despite continuing medical advances, the rate of newborn morbidity and m...
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Compositional Language Understanding with Textbased Relational Reasoning
Neural networks for natural language reasoning have largely focused on e...
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Weisfeiler and Leman Go Neural: Higherorder Graph Neural Networks
In recent years, graph neural networks (GNNs) have emerged as a powerful...
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Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning nod...
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Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Graph Convolutional Neural Networks for WebScale Recommender Systems
Recent advancements in deep neural networks for graphstructured data ha...
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Querying Complex Networks in Vector Space
Learning vector embeddings of complex networks is a powerful approach us...
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Community Interaction and Conflict on the Web
Users organize themselves into communities on web platforms. These commu...
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GraphRNN: A Deep Generative Model for Graphs
Modeling and generating graphs is fundamental for studying networks in b...
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Inductive Representation Learning on Large Graphs
Lowdimensional embeddings of nodes in large graphs have proved extremel...
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Community Identity and User Engagement in a MultiCommunity Landscape
A community's identity defines and shapes its internal dynamics. Our cur...
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Loyalty in Online Communities
Loyalty is an essential component of multicommunity engagement. When us...
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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
Words shift in meaning for many reasons, including cultural factors like...
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Inducing DomainSpecific Sentiment Lexicons from Unlabeled Corpora
A word's sentiment depends on the domain in which it is used. Computatio...
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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Understanding how words change their meanings over time is key to models...
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Efficient Learning and Planning with Compressed Predictive States
Predictive state representations (PSRs) offer an expressive framework fo...
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William L. Hamilton
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