50 citations · 117 across the 17 of their papers we have counts for
17 papers
Transfer Learning for Temporal Link Prediction
Ayan Chatterjee, Barbara Ikica, Babak Ravandi +1
Link prediction on graphs has applications spanning from recommender systems to drug discovery. Temporal link prediction (TLP) refers to predicting future links in a temporally evo…
BIG-Bench Extra Hard
Mehran Kazemi, Bahare Fatemi, Hritik Bansal +17
Large language models (LLMs) are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LL…
Entailed Between the Lines: Incorporating Implication into NLI
Shreya Havaldar, Hamidreza Alvari, John Palowitch +3
Much of human communication depends on implication, conveying meaning beyond literal words to express a wider range of thoughts, intentions, and feelings. For models to better unde…
Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions
Krisztian Balog, John Palowitch, Barbara Ikica +3
The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data…
SocialQuotes: Learning Contextual Roles of Social Media Quotes on the Web
John Palowitch, Hamidreza Alvari, Mehran Kazemi +2
Web authors frequently embed social media to support and enrich their content, creating the potential to derive web-based, cross-platform social media representations that can enab…
Into the Unknown: Generating Geospatial Descriptions for New Environments
Tzuf Paz-Argaman, John Palowitch, Sayali Kulkarni +2
Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires rea…
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning
Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin +6
Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex tem…
Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions
Tzuf Paz-Argaman, Sayali Kulkarni, John Palowitch +2
When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. Ho…
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
Mustafa Yasir, John Palowitch, Anton Tsitsulin +2
Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research. GraphWorld is a recent solution which uses…
Graph Generative Model for Benchmarking Graph Neural Networks
Minji Yoon, Yue Wu, John Palowitch +2
As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models o…
TF-GNN: Graph Neural Networks in TensorFlow
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais +24
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that…
Synthetic Graph Generation to Benchmark Graph Learning
Anton Tsitsulin, Benedek Rozemberczki, John Palowitch +1
Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, bec…
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
Minji Yoon, John Palowitch, Dustin Zelle +3
Data continuously emitted from industrial ecosystems such as social or e-commerce platforms are commonly represented as heterogeneous graphs (HG) composed of multiple node/edge typ…
GraphWorld: Fake Graphs Bring Real Insights for GNNs
John Palowitch, Anton Tsitsulin, Brandon Mayer +1
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful…
Recurrent Graph Neural Networks for Rumor Detection in Online Forums
Di Huang, Jacob Bartel, John Palowitch
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content. This work presents techniques for cl…
Graph Clustering with Graph Neural Networks
Anton Tsitsulin, John Palowitch, Bryan Perozzi +1
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised pr…
MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit
John Palowitch, Bryan Perozzi
Are Graph Neural Networks (GNNs) fair? In many real world graphs, the formation of edges is related to certain node attributes (e.g. gender, community, reputation). In this case, s…