Lulwah AlKulaib
  • Bio
  • Papers
  • Experience
  • Teaching
  • Recent & Upcoming Talks
    • Our paper got accepted at ASONAM 2024
  • Projects
  • Publications
    • Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    • Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    • Synthetic Arabic Medical Dialogues Using Advanced Multi-Agent LLM Techniques
    • Hypergraph Text Classification for Mental Health Misleading Advice
    • Analyzing Prediction of Depression and Anxiety on Reddit: a Multi-task Learning Approach through GMMTL
    • Analyzing Prediction of Depression and Anxiety on Reddit: a Multi-task Learning Approach through GMMTL
    • Balancing the Scales: HyperSMOTE for Enhanced Hypergraph Classification
    • Balancing the Scales: HyperSMOTE for Enhanced Hypergraph Classification
    • From Guest to Family: An Innovative Framework for Enhancing Memorable Experiences in the Hotel Industry
    • Hypergraph Text Classification for Mental Health Misleading Advice
    • The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination
    • The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination
    • UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset
    • UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset
    • VT News covered our best paper award from ASONAM 2022.
    • DOD-Explainer: Explainable Drug Overdose Deaths Predictor from Crime and Socioeconomic Data
    • DOD-Explainer: Explainable Drug Overdose Deaths Predictor from Crime and Socioeconomic Data
    • HyperTwitter: A Hypergraph-based Approach to Identify Influential Twitter Users and Tweets
    • HyperTwitter: A Hypergraph-based Approach to Identify Influential Twitter Users and Tweets
    • Predicting depression and anxiety on reddit: a multi-task learning approach
    • Predicting depression and anxiety on reddit: a multi-task learning approach
    • PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit
    • Twitter bot identification: An anomaly detection approach
    • Twitter bot identification: An anomaly detection approach
    • Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis
    • Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis
    • Collect Ethically: Reduce Bias in Twitter Datasets
    • Collect Ethically: Reduce Bias in Twitter Datasets
    • Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate
    • NYT coverage of our "Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate" paper.
    • Detecting and characterizing bot-like behavior on Twitter
    • Detecting and Characterizing Bot-Like Behavior on Twitter
    • Twitter Bots Multiclass Classification Using Bot-Like Behavior Features.
    • Twitter Bots Multiclass Classification Using Bot-Like Behavior Features.
    • Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate
    • Recognizing Images of Eating Disorders in Social Media.
  • Blog
    • 🎉 Easily create your own simple yet highly customizable blog
    • 🧠 Sharpen your thinking with a second brain
    • 📈 Communicate your results effectively with the best data visualizations
    • 👩🏼‍🏫 Teach academic courses
    • ✅ Manage your projects
  • Projects
    • Pandas
    • PyTorch
    • scikit-learn
  • Experience
  • Teaching

UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset

Jan 1, 2023·
Melike Yildiz Aktas
,
Lulwah AlKulaib
,
Chang-Tien Lu
· 0 min read
Cite
Type
Conference paper
Publication
2023 IEEE International Conference on Big Data (BigData)
Last updated on Jan 1, 2023
← The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination Jan 1, 2023
UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset Jan 1, 2023 →

© 2024 Lulwah AlKulaib. This work is licensed under CC BY NC ND 4.0