Search Results for author:
Found 50 papers, 17 papers with code
ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity
In this paper, we study invariance and continuity in the generative machine learning for CSP.
Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization
While Annealing Machines (AM) have shown increasing capabilities in solving complex combinatorial problems, positioning themselves as a more immediate alternative to the expected advances of future fully quantum solutions, there are still scaling limitations.
From Votes to Volatility Predicting the Stock Market on Election Day
To address this challenge, we propose the Election Day Stock Market Forecasting (EDSMF) Model.
GEFM: Graph-Enhanced EEG Foundation Model
To address this limitation, we propose Graph-Enhanced EEG Foundation Model (GEFM), a novel foundation model for EEG that integrates both temporal and inter-channel information.
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning
In diagnosing neurological disorders from electroencephalography (EEG) data, foundation models such as Transformers have been employed to capture temporal dynamics.
Multimodal Point-of-Interest Recommendation
A model trained on this semi-multimodal dataset has outperformed another model trained on the same dataset without picture descriptions.
A Prompting-Based Representation Learning Method for Recommendation with Large Language Models
The extensive information pre-trained by these LLMs allows for the potential to capture a more profound semantic representation from different contextual information of users and items.
Popular News Always Compete for the User's Attention! POPK: Mitigating Popularity Bias via a Temporal-Counterfactual
", POPK aims to improve recommendation accuracy and diversity.
A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System
In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains.
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs).
Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
Large language models (LLMs) have recently transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations.
Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs
Hence, as a reference implementation, we develop a simple neural model induced with EFT for capturing evolving graph spectra.
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs.
On Data Imbalance in Molecular Property Prediction with Pre-training
In this study, we propose an effective pre-training method that addresses the imbalance in input data.
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?
In material science, the prediction of unobserved values, commonly referred to as extrapolation, is particularly critical for property prediction as it enables researchers to gain insight into materials beyond the limits of available data.
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems.
Exploring 360-Degree View of Customers for Lookalike Modeling
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base.
Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?
$\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology.
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Ranked #2 on
Traffic Prediction
on EXPY-TKY
Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions
Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra.
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation
The smoothing is specially desired in the presence of homophilic graphs, such as the ones we find on recommender systems.
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks.
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks
Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges.
How Expressive are Transformers in Spectral Domain for Graphs?
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
SpecTRA: Spectral Transformer for Graph Representation Learning
Transformers have recently been applied in the more generic domain of graphs.
Efficient Scaling of Dynamic Graph Neural Networks
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems.
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.
Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling
In the case of distributed graph processing, changing the number of the graph partitions while maintaining high partitioning quality imposes serious computational overheads as typically a time-consuming graph partitioning algorithm needs to execute each time repartitioning is required.
graph partitioning
Distributed, Parallel, and Cluster Computing
Databases
Discrete Mathematics
Data Structures and Algorithms
Social and Information Networks
Global Data Science Project for COVID-19
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
The Impact of COVID-19 on Flight Networks
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections.
What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent.
Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincaré Embeddings
In the recent years money laundering schemes have grown in complexity and speed of realization, affecting financial institutions and millions of customers globally.
Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis
For the knowledge graph, we use the Nikkei Value Search data, which is a rich dataset showing mainly supplier relations among Japanese and foreign companies.
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms.
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Ranked #5 on
Dynamic Link Prediction
on DBLP Temporal
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.
Databases
Scalable Graph Learning for Anti-Money Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.
Scalable attribute-aware network embedding with locality
Here we propose \emph{SANE}, a scalable attribute-aware network embedding algorithm with locality, to learn the joint representation from topology and attributes.
Learning Graph Topological Features via GAN
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs.
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship.
Social and Information Networks Physics and Society
Building Graph Representations of Deep Vector Embeddings
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.
Full-Network Embedding in a Multimodal Embedding Pipeline
In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.
Can GAN Learn Topological Features of a Graph?
This paper is first-line research expanding GANs into graph topology analysis.
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.
Medical Text Classification using Convolutional Neural Networks
We present an approach to automatically classify clinical text at a sentence level.
Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm
We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction.
Data Structures and Algorithms Social and Information Networks Physics and Society
On the Behavior of Convolutional Nets for Feature Extraction
We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning.
Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
We present a scalable parallel solver for numerical constraint satisfaction problems (NCSPs).
Scalable Parallel Numerical CSP Solver
We present a parallel solver for numerical constraint satisfaction problems (NCSPs) that can scale on a number of cores.
