Search Results for author: Soji Adeshina

Found 12 papers, 6 papers with code

Hierarchical Compression of Text-Rich Graphs via Large Language Models

no code implementations13 Jun 2024 Shichang Zhang, Da Zheng, Jiani Zhang, Qi Zhu, Xiang Song, Soji Adeshina, Christos Faloutsos, George Karypis, Yizhou Sun

Large Language Models (LLMs), noted for their superior text understanding abilities, offer a solution for processing the text in graphs but face integration challenges due to their limitation for encoding graph structures and their computational complexities when dealing with extensive text in large neighborhoods of interconnected nodes.

Node Classification

GraphStorm: all-in-one graph machine learning framework for industry applications

1 code implementation10 Jun 2024 Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis

GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code.

graph construction

NetInfoF Framework: Measuring and Exploiting Network Usable Information

1 code implementation12 Feb 2024 Meng-Chieh Lee, Haiyang Yu, Jian Zhang, Vassilis N. Ioannidis, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos

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?

Graph Neural Network Link Prediction +1

Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs

1 code implementation20 Apr 2023 Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, Da Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos, George Karypis

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.

Knowledge Distillation Node Classification

OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization

no code implementations31 Jan 2023 Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu

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.

Node Classification

ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning

no code implementations9 Jun 2022 Zhenwei Dai, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis

ScatterSample employs a sampling module termed DiverseUncertainty to collect instances with large uncertainty from different regions of the sample space for labeling.

Active Learning Fraud Detection +1

TempoQR: Temporal Question Reasoning over Knowledge Graphs

1 code implementation10 Dec 2021 Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Soji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, George Karypis

The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings.

Entity Embeddings Graph Question Answering +4

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features

1 code implementation26 Oct 2021 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.

Graph Neural Network

Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation

no code implementations12 Oct 2021 Cole Hawkins, Vassilis N. Ioannidis, Soji Adeshina, George Karypis

Consistency training is a popular method to improve deep learning models in computer vision and natural language processing.

Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features

no code implementations ICLR 2022 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).

Relational Graph Neural Networks for Fraud Detection in a Super-App environment

no code implementations29 Jul 2021 Jaime D. Acevedo-Viloria, Luisa Roa, Soji Adeshina, Cesar Charalla Olazo, Andrés Rodríguez-Rey, Jose Alberto Ramos, Alejandro Correa-Bahnsen

Large digital platforms create environments where different types of user interactions are captured, these relationships offer a novel source of information for fraud detection problems.

Fraud Detection

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