150 citations · 553 across the 38 of their papers we have counts for
38 papers
ImgCoT: Compressing Long Chain of Thought into Compact Visual Tokens for Efficient Reasoning of Large Language Model
Xiaoshu Chen, Sihang Zhou, Ke Liang +2
Compressing long chains of thought (CoT) into compact latent tokens is crucial for efficient reasoning with large language models (LLMs). Recent studies employ autoencoders to achi…
Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
Meng Liu, Ke Liang, Siwei Wang +3
Temporal Graph Clustering (TGC) is a new task with little attention, focusing on node clustering in temporal graphs. Compared with existing static graph clustering, it can find the…
Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism
Xiaoshu Chen, Sihang Zhou, Ke Liang +5
Chain of thought (CoT) fine-tuning aims to endow large language models (LLMs) with reasoning capabilities by training them on curated reasoning traces. It leverages both supervised…
Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster
Xiao Chen, Sihang Zhou, Ke Liang +2
Chain-of-thought (CoT) distillation allows a large language model (LLM) to guide a small language model (SLM) in reasoning tasks. Existing methods train the SLM to learn the long r…
Distilling Reasoning Ability from Large Language Models with Adaptive Thinking
Xiaoshu Chen, Sihang Zhou, Ke Liang +1
Chain of thought finetuning (cot-finetuning) aims to endow small language models (SLM) with reasoning ability to improve their performance towards specific tasks by allowing them t…
One-Step Late Fusion Multi-view Clustering with Compressed Subspace
Qiyuan Ou, Pei Zhang, Sihang Zhou +1
Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clu…
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
Qiyuan Ou, Siwei Wang, Pei Zhang +2
Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up til…
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
Meng Liu, Ke Liang, Dayu Hu +6
Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-mod…
Reinforcement Graph Clustering with Unknown Cluster Number
Yue Liu, Ke Liang, Jun Xia +5
Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the pe…
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Xihong Yang, Jiaqi Jin, Siwei Wang +7
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the…
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Xihong Yang, Cheng Tan, Yue Liu +7
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distri…
Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning
Ke Liang, Sihang Zhou, Yue Liu +3
Multimodal knowledge graphs (MKGs), which intuitively organize information in various modalities, can benefit multiple practical downstream tasks, such as recommendation systems, a…
arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
Meng Liu, Ke Liang, Yue Liu +3
Temporal graph clustering (TGC) is a crucial task in temporal graph learning. Its focus is on node clustering on temporal graphs, and it offers greater flexibility for large-scale…
Message Intercommunication for Inductive Relation Reasoning
Ke Liang, Lingyuan Meng, Sihang Zhou +5
Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention. The models developed based on Graph Ind…
Dink-Net: Neural Clustering on Large Graphs
Yue Liu, Ke Liang, Jun Xia +4
Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the exis…
Deep Temporal Graph Clustering
Meng Liu, Yue Liu, Ke Liang +4
Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevert…
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning
Lingyuan Meng, Ke Liang, Bin Xiao +5
Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. Th…
RARE: Robust Masked Graph Autoencoder
Wenxuan Tu, Qing Liao, Sihang Zhou +5
Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perf…
GANN: Graph Alignment Neural Network for Semi-Supervised Learning
Linxuan Song, Wenxuan Tu, Sihang Zhou +2
Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labe…
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
Wenxuan Tu, Bin Xiao, Xinwang Liu +3
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer f…
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Meng Liu, Ke Liang, Yawei Zhao +5
Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +7
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample m…
Cluster-guided Contrastive Graph Clustering Network
Xihong Yang, Yue Liu, Sihang Zhou +6
Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recentl…
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
Ke Liang, Lingyuan Meng, Meng Liu +6
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research dir…