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Found 43 papers, 25 papers with code
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs
This paper studies few-shot relation extraction, which aims at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation.
Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science
Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines.
GraphText: Graph Reasoning in Text Space
Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data.
Learning on Large-scale Text-attributed Graphs via Variational Inference
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
Ranked #1 on
Node Property Prediction
on ogbn-papers100M
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs.
Neural Structured Prediction for Inductive Node Classification
This problem has been extensively studied with graph neural networks (GNNs) by learning effective node representations, as well as traditional structured prediction methods for modeling the structured output of node labels, e. g., conditional random fields (CRFs).
Structured Multi-task Learning for Molecular Property Prediction
However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity.
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
Joint Modeling of Visual Objects and Relations for Scene Graph Generation
We further propose an efficient and effective algorithm for inference based on mean-field variational inference, in which we first provide a warm initialization by independently predicting the objects and their relations according to the current model, followed by a few iterations of relational reasoning.
Multi-task Learning with Domain Knowledge for Molecular Property Prediction
In this paper, we study multi-task learning for molecule property prediction in a different setting, where a relation graph between different tasks is available.
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution.
Predicting Infectiousness for Proactive Contact Tracing
Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
Then in the E-step, we select a set of high-quality rules from all generated rules with both the rule generator and reasoning predictor via posterior inference; and in the M-step, the rule generator is updated with the rules selected in the E-step.
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
To more effectively generalize to new relations, in this paper we study the relationships between different relations and propose to leverage a global relation graph.
Graph Policy Network for Transferable Active Learning on Graphs
Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields.
COVI White Paper
Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections.
Continuous Graph Neural Networks
The key idea is how to characterise the continuous dynamics of node representations, i. e. the derivatives of node representations, w. r. t.
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
We present GraphMix, a regularization technique for Graph Neural Network based semi-supervised object classification, leveraging the recent advances in the regularization of classical deep neural networks.
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object classification, whereby we propose to train a fully-connected network jointly with the graph neural network via parameter sharing and interpolation-based regularization.
Ranked #1 on
Node Classification
on Pubmed random partition
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph
We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e. g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events.
Transfer Active Learning For Graph Neural Networks
Moreover, we also studied how to learn a universal policy for labeling nodes on graphs with multiple training graphs and then transfer the learned policy to unseen graphs.
Collaborative Policy Learning for Open Knowledge Graph Reasoning
We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
Therefore, in this paper we propose to study aligning knowledge graphs in fully-unsupervised or weakly-supervised fashion, i. e., without or with only a few aligned triplets.
Probabilistic Logic Neural Networks for Reasoning
In the E-step, a knowledge graph embedding model is used for inferring the missing triplets, while in the M-step, the weights of logic rules are updated based on both the observed and predicted triplets.
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks.
GMNN: Graph Markov Neural Networks
Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training.
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.
Ranked #1 on
Node Classification
on YouTube
Learning Dual Retrieval Module for Semi-supervised Relation Extraction
In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentence---two tasks are complementary to each other and can be optimized jointly for mutual enhancement.
Knowledge Graph Embedding with Hierarchical Relation Structure
To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
We propose a novel co-training framework with a distributional module and a pattern module.
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network.
Automatic Synonym Discovery with Knowledge Bases
In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus.
Identity-sensitive Word Embedding through Heterogeneous Networks
Based on an identity-labeled text corpora, a heterogeneous network of words and word identities is constructed to model different-levels of word co-occurrences.
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
It models vertices as low-dimensional vectors to explore network structure-embedded similarity.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Ranked #11 on
Relation Extraction
on NYT11-HRL
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task.
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
Ranked #5 on
Node Classification
on Wikipedia
