Search Results for author: Christos Faloutsos

Found 41 papers, 18 papers with code

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

ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases

no code implementations24 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 .

OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision

1 code implementation29 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.

Language Modelling

CGC: Contrastive Graph Clustering for Community Detection and Tracking

no code implementations5 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.

Community Detection Deep Clustering +3

Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series

no code implementations11 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?

Decision Making EEG +1

gen2Out: Detecting and Ranking Generalized Anomalies

1 code implementation6 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.

Anomaly Detection EEG

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

1 code implementation30 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?

Anomaly Detection

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

1 code implementation26 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.

Graph Mining Unity

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

3 code implementations20 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.

J-Recs: Principled and Scalable Recommendation Justification

no code implementations11 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.

AutoAudit: Mining Accounting and Time-Evolving Graphs

1 code implementation1 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

Real-Time Anomaly Detection in Edge Streams

3 code implementations17 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?

Anomaly Detection Anomaly Detection in Edge Streams

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

no code implementations22 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.

Octet: Online Catalog Taxonomy Enrichment with Self-Supervision

no code implementations18 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.

Term Extraction

Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations

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.

Graph Generation

Higher-Order Label Homogeneity and Spreading in Graphs

1 code implementation18 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.

Fraud Detection Recommendation Systems

Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage

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.

AutoBlock: A Hands-off Blocking Framework for Entity Matching

1 code implementation7 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.

Entity Resolution Representation Learning

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

3 code implementations11 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?

Anomaly Detection in Edge Streams

SEDANSPOT: Detecting Anomalies in Edge Streams

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.

Anomaly Detection in Edge Streams

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

1 code implementation4 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

HoloScope: Topology-and-Spike Aware Fraud Detection

1 code implementation6 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

The Many Faces of Link Fraud

no code implementations5 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.

Fraud Detection

FairJudge: Trustworthy User Prediction in Rating Platforms

no code implementations30 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.


Tensor Decomposition for Signal Processing and Machine Learning

no code implementations6 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).

Collaborative Filtering Tensor Decomposition

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

no code implementations19 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.

Bayesian Inference Fraud Detection

Linearized and Single-Pass Belief Propagation

1 code implementation27 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").

Universal and Distinct Properties of Communication Dynamics: How to Generate Realistic Inter-event Times

no code implementations19 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.

NetSimile: A Scalable Approach to Size-Independent Network Similarity

no code implementations12 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

Cost-effective Outbreak Detection in Networks

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.

Sampling From Large Graphs

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.

Graph Sampling

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