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Found 62 papers, 33 papers with code
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
Entity6K: A Large Open-Domain Evaluation Dataset for Real-World Entity Recognition
Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments.
McCatch: Scalable Microcluster Detection in Dimensional and Nondimensional Datasets
How could we have an outlier detector that works even with nondimensional data, and ranks together both singleton microclusters ('one-off' outliers) and nonsingleton microclusters by their anomaly scores?
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM
We have developed the \textbf{SnapNTell Dataset}, distinct from traditional VQA datasets: (1) It encompasses a wide range of categorized entities, each represented by images and explicitly named in the answers; (2) It features QA pairs that require extensive knowledge for accurate responses.
Automatic Question-Answer Generation for Long-Tail Knowledge
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA).
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well?
EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series
We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers; (ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e. g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time.
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Recent advances in tabular data generation have greatly enhanced synthetic data quality.
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
ExplainFix: Explainable Spatially Fixed Deep Networks
We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
However, GNN explanation for link prediction (LP) is lacking in the literature.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
NetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks?
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself.
HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection
HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart.
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called MetaGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations.
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
ScatterSample employs a sampling module termed DiverseUncertainty to collect instances with large uncertainty from different regions of the sample space for labeling.
ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases
It outperforms the lightgbm2 by up to 34 pcp ROC-AUC in a cold start case when a new seller sells a new product .
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data.
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
Coarse-to-Fine Sparse Sequential Recommendation
We propose to model user dynamics from shopping intents and interacted items simultaneously.
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)?
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series
Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible?
gen2Out: Detecting and Ranking Generalized Anomalies
Our main contribution is the gen2Out algorithm, that has the following desirable properties: (a) Principled and Sound anomaly scoring that obeys the axioms for detectors, (b) Doubly-general in that it detects, as well as ranks generalized anomaly -- both point- and group-anomalies, (c) Scalable, it is fast and scalable, linear on input size.
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs?
Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
We first define a unified framework UNIFIEDGM that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks.
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
This allows us to detect sudden changes in the importance of any node.
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
While much research on distribution shift has focused on changes in the data domain, our work calls attention to rethink generalization for learning dynamical systems.
J-Recs: Principled and Scalable Recommendation Justification
Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users.
AutoAudit: Mining Accounting and Time-Evolving Graphs
How can we spot money laundering in large-scale graph-like accounting datasets?
Social and Information Networks
Real-Time Anomaly Detection in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts.
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment.
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
We provide a new graph generator, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.
Higher-Order Label Homogeneity and Spreading in Graphs
Vertices with stronger connections participate in higher-order structures in graphs, which calls for methods that can leverage these structures in the semi-supervised learning tasks.
Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
Ranked #1 on
Anomaly Detection in Edge Streams
on Darpa
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)?
SEDANSPOT: Detecting Anomalies in Edge Streams
Given a stream of edges from a time-evolving (un)weighted (un)directed graph, we consider the problem of detecting anomalous edges in near real-time using sublinear memory.
Ranked #2 on
Anomaly Detection in Edge Streams
on Darpa
Did We Get It Right? Predicting Query Performance in E-commerce Search
In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not.
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Knowledge graphs have emerged as an important model for studying complex multi-relational data.
Out-of-Core and Distributed Algorithms for Dense Subtensor Mining
Can we detect it when data are too large to fit in memory or even on a disk?
Databases Distributed, Parallel, and Cluster Computing Social and Information Networks H.2.8
HoloScope: Topology-and-Spike Aware Fraud Detection
Hence, we propose HoloScope, which uses information from graph topology and temporal spikes to more accurately detect groups of fraudulent users.
Social and Information Networks
The Many Faces of Link Fraud
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior.
FairJudge: Trustworthy User Prediction in Rating Platforms
We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product.
Tensor Decomposition for Signal Processing and Machine Learning
Tensors or {\em multi-way arrays} are functions of three or more indices $(i, j, k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r, c)$ for (row, column).
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
How can we detect suspicious users in large online networks?
Linearized and Single-Pass Belief Propagation
Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily ("birds of a feather flock together") or heterophily ("opposites attract").
Universal and Distinct Properties of Communication Dynamics: How to Generate Realistic Inter-event Times
We show the potential application of SFP by proposing a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications (e. g. phone calls, e-mails, comments on blogs) and an algorithm to detect anomalies.
NetSimile: A Scalable Approach to Size-Independent Network Similarity
Having such features will enable a wealth of graph mining tasks, including clustering, outlier detection, visualization, etc.
Social and Information Networks Physics and Society Applications
Cost-effective Outbreak Detection in Networks
We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude.
Sampling From Large Graphs
Thus graph sampling is essential. The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e. g., the diameter), to get estimates for the large graph.
