Christos Nick Faloutsos

About

Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds Ph.D. and M.Sc. degrees from the University of Toronto and a B.Sc. from the National Technical University of Athens. His research interests encompass anomaly and fraud detection in graphs and time series, human trafficking detection, fractals, self-similarity, and power laws. He is also engaged in indexing and data mining for video, biological, and medical databases. Faloutsos has contributed to a wide array of projects including stream mining, graph mining, biomedical and network data analysis using tensors, and image mining in biological contexts. His work extends to motion capture indexing, robust inter-domain routing, and interactive biological image search. He has been involved in ongoing efforts to detect human trafficking and electronic bee veterinary frameworks, as well as fraud detection in phone call and financial networks. His research has been supported by numerous NSF grants and collaborations with industry partners. Recognized for his influence in the field, Faloutsos has been featured in various media outlets and received awards such as the KDD'16 best paper award for his work on detecting fake social media followers and reviews.

Research topics

• Artificial Intelligence
• Computer Science
• Data Mining
• Computer Security
• Data science
• World Wide Web
• Database
• Human–computer interaction
• Mathematics

Selected publications

• Opinion Fraud Detection in Online Reviews by Network Effects

  Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 400 citations

  Senior authorCorresponding

  User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike th…

  DOI

• Midas: Microcluster-Based Detector of Anomalies in Edge Streams

  2020-04-03 · 91 citations

  articleSenior author

  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? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. Midas has t…

  PublisherDOI

• P-Companion

  2020 · 66 citations

  If one customer buys a tennis racket, what are the best 3 complementary products to purchase together? 3 tennis ball packs, 3 headbands, 3 overgrips, or 1 of each respectively? Complementary product recommendation (CPR), aiming at providing product suggestions that are often bought together to serve a joint demand, forms a pivotal component of e-commerce service, however, existing methods are far from optimal. Given one product, how to recommend its complementary products of different types is t…

  DOI

• TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning

  2024-07-10 · 5 citations

  articleSenior author

  How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks? Graph Neural Networks (GNNs) have become the state-of-the-art approach for many high-impact, real-world graph applications. For feature-rich graphs, a prevalent practice involves directly utilizing a PM to generate features. Nevertheless, this practice is suboptimal as the node features extracted from PMs are graph-agnostic and prevent GNNs from fully utilizing the potential…

  PublisherDOI

• Principled Mining, Forecasting, and Monitoring of Honeybee Time Series with EBV+

  ACM Transactions on Knowledge Discovery from Data · 2025-02-21 · 4 citations

  articleOpen access

  Honeybees, as natural crop pollinators, play a significant role in biodiversity and food production for human civilization. Bees actively regulate hive temperature (homeostasis) to maintain a colony’s proper functionality. Deviations from usual thermoregulation behavior due to external stressors (e.g., extreme environmental temperature, parasites, pesticide exposure) indicate an impending colony collapse. Anticipating such threats by forecasting hive temperature and finding changes in temperatur…

  PublisherOA PDFDOI

Recent grants

• Finding Patterns and Anomalies in Large Time-Evolving Graphs

  NSF · $338k · 2006–2009

• III-COR: Collaborative Research: Mining Biomedical and Network Data Using Tensors

  NSF · $308k · 2007–2010

• III: Small: Influence and Virus Propagation in Large Graphs - Theory and Algorithms

  NSF · $500k · 2010–2013

Frequent coauthors

• Danai Koutra

  101 shared

• Bryan Hooi

  73 shared

• Evangelos E. Papalexakis

  University of California, Riverside

  52 shared

• Rakesh Agrawal

  51 shared

• Leman Akoglu

  Carnegie Mellon University

  50 shared

• Daniel Barbará

  George Mason University

  50 shared

• Stefano Ceri

  Politecnico di Milano

  50 shared

• Christian S. Jensen

  50 shared

Labs

• Wellness NetworkPI

  Not provided

Education

• B.S.

  Nat. Tech. U. Athens

• M.S.

  University of Toronto

• Ph.D.

  University of Toronto

Awards & honors

• Ranked among the top 50 nurturers in information technology…
• Mentioned in the Greek newspaper 'To Vima' (2005)
• Distinguished Database Profiles in ACM SIGMOD record (2005)
• CMU press release about the NetProbe project for auction fra…
• NSF press release 12-187 on BIG DATA grants (2012)

Similar researchers at Carnegie Mellon University

• William W Cohenh-84
• Robert E Kassh-71
• Barnabas Poczosh-58