Search Results for author: Nitesh V. Chawla

Found 21 papers, 11 papers with code

Few-Shot Learning on Graphs: A Survey

no code implementations17 Mar 2022 Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.

Few-Shot Learning Graph Mining +1

Predicting Terrorist Attacks in the United States using Localized News Data

no code implementations12 Jan 2022 Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla

From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our novel feature representation method in such a context.

Graph Barlow Twins: A self-supervised representation learning framework for graphs

1 code implementation4 Jun 2021 Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla

The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling.

Contrastive Learning Graph Representation Learning +1

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

1 code implementation5 May 2021 Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla

An important advantage of DeepSMOTE over GAN-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection.

Few-Shot Graph Learning for Molecular Property Prediction

1 code implementation16 Feb 2021 Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla

The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.

Drug Discovery Graph Learning +3

AttrE2vec: Unsupervised Attributed Edge Representation Learning

no code implementations29 Dec 2020 Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla

Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a variety of downstream learning tasks.

Edge Classification Representation Learning

Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

1 code implementation11 Jun 2020 Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla

The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e. g., hourly, weekly, and weekday patterns).

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being

no code implementations10 Jun 2020 Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martinez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind D. Dey, Julie Gregg, Ted Grover, Stephen M. Mattingly, Shayan Mirjafari, Edward Moskal, Aaron Striegel, Nitesh V. Chawla

In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance.

Few-Shot Knowledge Graph Completion

1 code implementation26 Nov 2019 Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla

Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.

Knowledge Graph Completion One-Shot Learning

HONEM: Learning Embedding for Higher Order Networks

no code implementations15 Aug 2019 Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years.

Feature Engineering Link Prediction +2

FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings

1 code implementation6 Apr 2019 Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla

Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.

Dynamic graph embedding Incremental Learning +1

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

5 code implementations20 Nov 2018 Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

Time Series Unsupervised Anomaly Detection

Detecting Anomalies in Sequential Data with Higher-order Networks

1 code implementation27 Dec 2017 Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla

A major branch of anomaly detection methods relies on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure.

Social and Information Networks Physics and Society

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

no code implementations1 Jun 2017 Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm.

Will This Paper Increase Your h-index? Scientific Impact Prediction

2 code implementations15 Dec 2014 Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare.

Social and Information Networks Digital Libraries Physics and Society H.2.8; H.3.7

Predicting Online Video Engagement Using Clickstreams

no code implementations20 May 2014 Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla

In the nascent days of e-content delivery, having a superior product was enough to give companies an edge against the competition.

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