Search Results for author:
Found 45 papers, 34 papers with code
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion
Its general process is to first encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs.
Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration
We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e. g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques.
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
We present ImageReward -- the first general-purpose text-to-image human preference reward model -- to address various prevalent issues in generative models and align them with human values and preferences.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.
Ranked #19 on
Code Generation
on HumanEval
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.
Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit
Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (CMRC2018)
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
By updating only 0. 1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated.
GraphMAE: Self-Supervised Masked Graph Autoencoders
Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.
Ranked #1 on
Node Classification
on Cora: fixed 20 node per class
IDPG: An Instance-Dependent Prompt Generation Method
Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.
GRAND+: Scalable Graph Random Neural Networks
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on
Node Classification
on MAG-scholar-C
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on
Node Property Prediction
on ogbn-papers100M
Adaptive Diffusion in Graph Neural Networks
Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
A Self-supervised Method for Entity Alignment
We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.
TDGIA:Effective Injection Attacks on Graph Neural Networks
In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Ranked #1 on
Knowledge Graphs
on WikiKG90M-LSC
Understanding WeChat User Preferences and "Wow" Diffusion
"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.
Graph Representation Learning
Social and Information Networks
OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services
Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
MATCH: Metadata-Aware Text Classification in A Large Hierarchy
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.
Generalizing Graph Convolutional Networks
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
A Large-Scale Database for Graph Representation Learning
With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique.
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Heterogeneous Graph Transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
Ranked #17 on
Node Property Prediction
on ogbn-mag
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation
In this paper, we revisit the mathematical foundation of GCNs and study how to extend their representation capacity.
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods.
DeepInf: Social Influence Prediction with Deep Learning
Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence.
Neural Tensor Factorization
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data.
Revisiting Knowledge Base Embedding as Tensor Decomposition
We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
We study the problem of representation learning in heterogeneous networks.
Ranked #5 on
Link Prediction
on MovieLens 25M
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations
We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s.
Digital Libraries Social and Information Networks Physics and Society
Will This Paper Increase Your h-index? Scientific Impact Prediction
The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare.
Social and Information Networks Digital Libraries Physics and Society H.2.8; H.3.7
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Our model can predict social status of individuals with 93% accuracy.
