Christos Faloutsos

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Amazon ∙ Carnegie Mellon University

Professor at Carnegie Mellon University- Dept. of Computer Science

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Given sensor readings over time from a power grid, how can we accurately...
12/30/2020 ∙ by Shimiao Li, et al. ∙ 2 ∙ share

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Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

Graph data is ubiquitous in academia and industry, from social networks ...
11/26/2020 ∙ by Minji Yoon, et al. ∙ 6 ∙ share

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Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach

Given a dynamic graph stream, how can we detect the sudden appearance of...
11/26/2020 ∙ by Minji Yoon, et al. ∙ 2 ∙ share

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Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

How can we learn a dynamical system to make forecasts, when some variabl...
11/20/2020 ∙ by Rui Wang, et al. ∙ 3 ∙ share

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J-Recs: Principled and Scalable Recommendation Justification

Online recommendation is an essential functionality across a variety of ...
11/11/2020 ∙ by Namyong Park, et al. ∙ 2 ∙ share

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Real-Time Streaming Anomaly Detection in Dynamic Graphs

Given a stream of graph edges from a dynamic graph, how can we assign an...
09/17/2020 ∙ by Siddharth Bhatia, et al. ∙ 27 ∙ share

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AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

Can one build a knowledge graph (KG) for all products in the world? Know...
06/24/2020 ∙ by Xin Luna Dong, et al. ∙ 12 ∙ share

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MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Given multiple input signals, how can we infer node importance in a know...
06/22/2020 ∙ by Namyong Park, et al. ∙ 20 ∙ share

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Octet: Online Catalog Taxonomy Enrichment with Self-Supervision

Taxonomies have found wide applications in various domains, especially o...
06/18/2020 ∙ by Yuning Mao, et al. ∙ 0 ∙ share

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Higher-Order Label Homogeneity and Spreading in Graphs

Do higher-order network structures aid graph semi-supervised learning? G...
02/18/2020 ∙ by Dhivya Eswaran, et al. ∙ 0 ∙ share

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AutoBlock: A Hands-off Blocking Framework for Entity Matching

Entity matching seeks to identify data records over one or multiple data...
12/07/2019 ∙ by Wei Zhang, et al. ∙ 0 ∙ share

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MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

Given a stream of graph edges from a dynamic graph, how can we assign an...
11/11/2019 ∙ by Siddharth Bhatia, et al. ∙ 13 ∙ share

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Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

How can we estimate the importance of nodes in a knowledge graph (KG)? A...
05/21/2019 ∙ by Namyong Park, et al. ∙ 6 ∙ share

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Did We Get It Right? Predicting Query Performance in E-commerce Search

In this paper, we address the problem of evaluating whether results serv...
08/01/2018 ∙ by Rohan Kumar, et al. ∙ 0 ∙ share

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LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Knowledge graphs have emerged as an important model for studying complex...
07/23/2018 ∙ by Rakshit Trivedi, et al. ∙ 0 ∙ share

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DiSLR: Distributed Sampling with Limited Redundancy For Triangle Counting in Graph Streams

Given a web-scale graph that grows over time, how should its edges be st...
02/12/2018 ∙ by Kijung Shin, et al. ∙ 0 ∙ share

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Out-of-Core and Distributed Algorithms for Dense Subtensor Mining

How can we detect fraudulent lockstep behavior in large-scale multi-aspe...
02/04/2018 ∙ by Kijung Shin, et al. ∙ 0 ∙ share

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DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

Consider a stream of retweet events - how can we spot fraudulent lock-st...
06/11/2017 ∙ by Kijung Shin, et al. ∙ 0 ∙ share

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FairJudge: Trustworthy User Prediction in Rating Platforms

Rating platforms enable large-scale collection of user opinion about ite...
03/30/2017 ∙ by Srijan Kumar, et al. ∙ 0 ∙ share

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Tensor Decomposition for Signal Processing and Machine Learning

Tensors or multi-way arrays are functions of three or more indices (i,j...
07/06/2016 ∙ by Nicholas D. Sidiropoulos, et al. ∙ 0 ∙ share

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BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Review fraud is a pervasive problem in online commerce, in which fraudul...
11/19/2015 ∙ by Bryan Hooi, et al. ∙ 0 ∙ share

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TribeFlow: Mining & Predicting User Trajectories

Which song will Smith listen to next? Which restaurant will Alice go to ...
11/03/2015 ∙ by Flavio Figueiredo, et al. ∙ 0 ∙ share

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Linearized and Single-Pass Belief Propagation

How can we tell when accounts are fake or real in a social network? And ...
06/27/2014 ∙ by Wolfgang Gatterbauer, et al. ∙ 0 ∙ share

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Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization

How can we correlate neural activity in the human brain as it responds t...
02/28/2013 ∙ by Evangelos E. Papalexakis, et al. ∙ 0 ∙ share

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Christos Faloutsos

Featured Co-authors

Wei Zhang

Rui Wang

Jiawei Han

Andrew McCallum

Hongyuan Zha

Jun Ma

Rui Liu

Stephan Günnemann

Nicholas D. Sidiropoulos

Evangelos E. Papalexakis

Chenwei Zhang

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

J-Recs: Principled and Scalable Recommendation Justification

Real-Time Streaming Anomaly Detection in Dynamic Graphs

AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Octet: Online Catalog Taxonomy Enrichment with Self-Supervision

Higher-Order Label Homogeneity and Spreading in Graphs

AutoBlock: A Hands-off Blocking Framework for Entity Matching

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

Did We Get It Right? Predicting Query Performance in E-commerce Search

LinkNBed: Multi-Graph Representation Learning with Entity Linkage

DiSLR: Distributed Sampling with Limited Redundancy For Triangle Counting in Graph Streams

Out-of-Core and Distributed Algorithms for Dense Subtensor Mining

DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

FairJudge: Trustworthy User Prediction in Rating Platforms

Tensor Decomposition for Signal Processing and Machine Learning

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

TribeFlow: Mining & Predicting User Trajectories

Linearized and Single-Pass Belief Propagation

Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization