1 code implementation • 28 Apr 2024 • Minjie Wang, Quan Gan, David Wipf, Zhenkun Cai, Ning li, Jianheng Tang, Yanlin Zhang, Zizhao Zhang, Zunyao Mao, Yakun Song, Yanbo Wang, Jiahang Li, Han Zhang, Guang Yang, Xiao Qin, Chuan Lei, Muhan Zhang, Weinan Zhang, Christos Faloutsos, Zheng Zhang
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
1 code implementation • NeurIPS 2023 • Namyong Park, Ryan Rossi, Xing Wang, Antoine Simoulin, Nesreen Ahmed, Christos Faloutsos
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.
no code implementations • 19 Mar 2024 • JieLin Qiu, William Han, Winfred Wang, Zhengyuan Yang, Linjie Li, JianFeng Wang, Christos Faloutsos, Lei LI, Lijuan Wang
Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments.
no code implementations • 12 Mar 2024 • Braulio V. Sánchez Vinces, Robson L. F. Cordeiro, Christos Faloutsos
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?
no code implementations • 7 Mar 2024 • JieLin Qiu, Andrea Madotto, Zhaojiang Lin, Paul A. Crook, Yifan Ethan Xu, Xin Luna Dong, Christos Faloutsos, Lei LI, Babak Damavandi, Seungwhan Moon
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.
no code implementations • 3 Mar 2024 • Rohan Kumar, Youngmin Kim, Sunitha Ravi, Haitian Sun, Christos Faloutsos, Ruslan Salakhutdinov, Minji Yoon
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA).
no code implementations • 27 Feb 2024 • Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos
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.
1 code implementation • 22 Feb 2024 • Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis
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.
1 code implementation • 12 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?
no code implementations • 2 Feb 2024 • Mst. Shamima Hossain, Christos Faloutsos, Boris Baer, Hyoseung Kim, Vassilis J. Tsotras
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.
1 code implementation • 30 Oct 2023 • Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
1 code implementation • 14 Oct 2023 • Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, Zhengyuan Shen, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, George Karypis
Recent advances in tabular data generation have greatly enhanced synthetic data quality.
1 code implementation • 25 Sep 2023 • Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
no code implementations • 4 Jul 2023 • Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun, Wei Wang
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
1 code implementation • 20 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.
1 code implementation • 18 Mar 2023 • Alex Gaudio, Christos Faloutsos, Asim Smailagic, Pedro Costa, Aurelio Campilho
We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.
1 code implementation • 24 Feb 2023 • Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun
However, GNN explanation for link prediction (LP) is lacking in the literature.
no code implementations • 31 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.
1 code implementation • 31 Dec 2022 • Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos
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?
1 code implementation • 8 Oct 2022 • Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos
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.
1 code implementation • 5 Oct 2022 • Elvin Johnson, Shreshta Mohan, Alex Gaudio, Asim Smailagic, Christos Faloutsos, Aurélio Campilho
HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart.
1 code implementation • 18 Jun 2022 • Namyong Park, Ryan Rossi, Nesreen Ahmed, Christos Faloutsos
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.
no code implementations • 9 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.
no code implementations • 24 May 2022 • Bo He, Xiang Song, Vincent Gao, Christos Faloutsos
It outperforms the lightgbm2 by up to 34 pcp ROC-AUC in a cold start case when a new seller sells a new product .
1 code implementation • 29 Apr 2022 • Xinyang Zhang, Chenwei Zhang, Xian Li, Xin Luna Dong, Jingbo Shang, Christos Faloutsos, Jiawei Han
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.
1 code implementation • 5 Apr 2022 • Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, Christos Faloutsos
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.
no code implementations • 4 Apr 2022 • Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel, Julian McAuley
We propose to model user dynamics from shopping intents and interacted items simultaneously.
1 code implementation • 15 Feb 2022 • Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
no code implementations • 11 Nov 2021 • Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi, Jonathan Elmer, Christos Faloutsos, Leman Akoglu
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?
1 code implementation • 6 Sep 2021 • Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos, T. Noah Hutson, Leon Iasemidis
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.
1 code implementation • 30 Dec 2020 • Shimiao Li, Amritanshu Pandey, Bryan Hooi, Christos Faloutsos, Larry Pileggi
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs?
1 code implementation • 26 Nov 2020 • Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos
This allows us to detect sudden changes in the importance of any node.
1 code implementation • 26 Nov 2020 • Minji Yoon, Théophile Gervet, Bryan Hooi, Christos Faloutsos
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.
3 code implementations • 20 Nov 2020 • Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu
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.
no code implementations • 11 Nov 2020 • Namyong Park, Andrey Kan, Christos Faloutsos, Xin Luna Dong
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.
1 code implementation • 1 Nov 2020 • Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre Jinghong Liang, Leman Akoglu, Vincent S. Tseng, Christos Faloutsos
How can we spot money laundering in large-scale graph-like accounting datasets?
Social and Information Networks
3 code implementations • 17 Sep 2020 • Siddharth Bhatia, Rui Liu, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos
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?
no code implementations • 24 Jun 2020 • Xin Luna Dong, Xiang He, Andrey Kan, Xi-An Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han
Can one build a knowledge graph (KG) for all products in the world?
no code implementations • 22 Jun 2020 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
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.
no code implementations • 18 Jun 2020 • Yuning Mao, Tong Zhao, Andrey Kan, Chenwei Zhang, Xin Luna Dong, Christos Faloutsos, Jiawei Han
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.
1 code implementation • 2020 • Jure Leskovec, Jon Kleinberg, Christos Faloutsos
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.
1 code implementation • 18 Feb 2020 • Dhivya Eswaran, Srijan Kumar, Christos Faloutsos
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.
no code implementations • AKBC 2020 • Varun Embar, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Christos Faloutsos, Lise Getoor
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
1 code implementation • 7 Dec 2019 • Wei Zhang, Hao Wei, Bunyamin Sisman, Xin Luna Dong, Christos Faloutsos, David Page
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
9 code implementations • 11 Nov 2019 • Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos
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
no code implementations • 21 May 2019 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
How can we estimate the importance of nodes in a knowledge graph (KG)?
1 code implementation • ICDM 2018 • Dhivya Eswaran, Christos Faloutsos
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
no code implementations • 1 Aug 2018 • Rohan Kumar, Mohit Kumar, Neil Shah, Christos Faloutsos
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.
no code implementations • ACL 2018 • Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong
Knowledge graphs have emerged as an important model for studying complex multi-relational data.
1 code implementation • 4 Feb 2018 • Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos
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
1 code implementation • 6 May 2017 • Shenghua Liu, Bryan Hooi, Christos Faloutsos
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
no code implementations • 5 Apr 2017 • Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
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.
no code implementations • 30 Mar 2017 • Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, V. S. Subrahamanian
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.
no code implementations • 6 Jul 2016 • Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E. Papalexakis, Christos Faloutsos
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).
no code implementations • 19 Nov 2015 • Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
no code implementations • 3 Nov 2015 • Flavio Figueiredo, Bruno Ribeiro, Jussara Almeida, Christos Faloutsos
Which song will Smith listen to next?
no code implementations • 15 Oct 2014 • Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
How can we detect suspicious users in large online networks?
1 code implementation • 27 Jun 2014 • Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos
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").
no code implementations • 19 Mar 2014 • Pedro O. S. Vaz de Melo, Christos Faloutsos, Renato Assunção, Rodrigo Alves, Antonio A. F. Loureiro
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.
no code implementations • 12 Sep 2012 • Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos
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
1 code implementation • SIGKDD 2007 • Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, Natalie Glance
We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude.
1 code implementation • KDD 2006 • Jure Leskovec, Christos Faloutsos
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.