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Found 36 papers, 23 papers with code
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification
Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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A 2D Semantic-Aware Position Encoding for Vision Transformers
Traditional approaches like absolute position encoding and relative position encoding primarily focus on 1D linear position relationship, often neglecting the semantic similarity between distant yet contextually related patches.
Rethinking Time Encoding via Learnable Transformation Functions
While previous methods focus on capturing time patterns, many rely on specific inductive biases, such as using trigonometric functions to model periodicity.
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Existing TGNNs embed texts statically and rely heavily on encoding mechanisms that biasedly prioritize structural information, overlooking the temporal evolution of text semantics and the essential interplay between semantics and structures for synergistic reinforcement.
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack
Recent advancements in Large Language Models (LLMs) have expanded their context windows to unprecedented lengths, sparking debates about the necessity of Retrieval-Augmented Generation (RAG).
Enhancing Masked Time-Series Modeling via Dropping Patches
This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series.
An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis
Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series.
Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need
To address these challenges, we introduce Mixture of Universals (MoU), a versatile model to capture both short-term and long-term dependencies for enhancing performance in time series forecasting.
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners.
Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks
Retrieval-augmented Generation (RAG) has markedly enhanced the capabilities of Large Language Models (LLMs) in tackling knowledge-intensive tasks.
Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling
These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node.
Can Graph Learning Improve Planning in LLM-based Agents?
This theoretical insight led us to integrate GNNs with LLMs to enhance overall performance.
Finetuning Large Language Model for Personalized Ranking
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, motivating researchers to investigate their potential use in recommendation systems.
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
Drug combinations can cause adverse drug-drug interactions(DDIs).
DiffPoint: Single and Multi-view Point Cloud Reconstruction with ViT Based Diffusion Model
As the task of 2D-to-3D reconstruction has gained significant attention in various real-world scenarios, it becomes crucial to be able to generate high-quality point clouds.
Prompt Learning on Temporal Interaction Graphs
In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks.
Retrieval-Augmented Generation for Large Language Models: A Survey
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.
Graph Prompt Learning: A Comprehensive Survey and Beyond
This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications.
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification
Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively.
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning
Data provenance analysis on provenance graphs has emerged as a common approach in APT detection.
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
To overcome the indiscriminate updating issue, we introduce the Adaptive Short-term Updater module that will automatically discard the useless or noisy edges, ensuring iLoRE's effectiveness and instant ability.
RDGSL: Dynamic Graph Representation Learning with Structure Learning
To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph.
SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding
Simultaneously, resource consumption of a single-GPU can be diminished by up to 69%, thus enabling the multiple GPU-based training and acceleration encompassing millions of nodes and billions of edges.
Dual Intents Graph Modeling for User-centric Group Discovery
Therefore, user-centric group discovery task, i. e., recommending groups to users can help both users' online experiences and platforms' long-term developments.
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks.
TIGER: Temporal Interaction Graph Embedding with Restarts
However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation
We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models.
ReCo: A Dataset for Residential Community Layout Planning
In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date.
Triangle Graph Interest Network for Click-through Rate Prediction
Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors.
Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
In this paper, we propose an approach to bridge pre-trained models and code-related tasks.
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead.
ISTD-GCN: Iterative Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Speed Forecasting
Most of the existing algorithms for traffic speed forecasting split spatial features and temporal features to independent modules, and then associate information from both dimensions.
Social and Information Networks
