Danai Koutra is an Assistant Professor in Computer Science and Engineering at University of Michigan, Ann Arbor. Her research interests include large-scale graph mining, graph similarity and matching, graph summarization, and anomaly detection. Danai's research has been applied mainly to social, collaboration and web networks, as well as brain connectivity graphs. She holds one "rate-1" patent and has six (pending) patents on bipartite graph alignment. Danai has multiple papers in top data mining conferences, including 2 award-winning papers, and her work has been covered by the popular press, such as the MIT Technology Review. She has worked at IBM Hawthorne, Microsoft Research Redmond, and Technicolor Palo Alto/Los Altos. She earned her Ph.D. and M.S. in Computer Science from CMU in 2015 and her diploma in Electrical and Computer Engineering at the National Technical University of Athens in 2010. 

@InProceedings{   ShahKZGF15,
  author        = {Neil Shah and Danai Koutra and Tianmin Zou and 
                   Brian Gallagher and Christos Faloutsos},
  title         = {{TimeCrunch: Interpretable Dynamic Graph 
                   Summarization}},
  booktitle     = {Proceedings of the 21st ACM International 
                   Conference on Knowledge Discovery and Data 
                   Mining (SIGKDD)},
  year          = {2015}
}



@InProceedings{   ShahKZGF15,
  author        = {Pravallika Devineni and Danai Koutra and
                   Michalis Faloutsos and Christos Faloutsos},
  title         = {{If walls could talk: Patterns and anomalies
                   in Facebook wallposts }},
  booktitle     =  {Proceedings of the IEEE/ACM International  
                   Conference on Advances in Social Networks 
                   Analysis and Mining  (ASONAM)},
  year          = {2015}
}



@inproceedings{ KoutraBH15,
	author = {Danai Koutra and Paul N. Bennett and Eric Horvitz},
	title = {{Events and Controversies: Influences of a Shocking News 
		Event on Information Seeking}},
	booktitle = {{24th International World Wide Web Conference (WWW)}},
	year = {2015}
}





@article{ GatterbauerGKF14,
author= {Wolfgang Gatterbauer and
Stephan G{\"{u}}nnemann and
Danai Koutra and
Christos Faloutsos},
title = {{Linearized and Single-Pass Belief Propagation}},
journal   = {{PVLDB}},
year  = {2014},
volume= {8},
issue = {4},
note  = {{To be presented at the 41st International Conference on Very
Large Data Bases, August 31st - September 4th, 2015, Kohala
Coast, Hawaii}}
}


@inproceedings{ LaseckiGKJDB14,
author= {Walter S. Lasecki and
Mitchell Gordon and
Danai Koutra and
Malte F. Jung and
Steven P. Dow and
Jeffrey P. Bigham},
title = {Glance: rapidly coding behavioral video with the crowd},
booktitle = {The 27th Annual {ACM} Symposium on User Interface Software
and Technology, {UIST} '14, Honolulu, HI, USA, October 5-8, 2014},
year  = {2014},
pages = {551--562},
}


@InProceedings{	KoutraBH14_TAIA,
author= {Danai Koutra and
Paul N. Bennett and
Eric Horvitz},
title = {Influences of a Shocking News Event on Information Seeking},
journal   = {SIGIR 2014 Workshop on Temporal, Social and Spatially-aware
Information Access (TAIA)},
year  = {2014},
}



@inproceedings{DBLP:conf/pakdd/KangLKF14,
author= {U. Kang and
Jay Yoon Lee and
Danai Koutra and
Christos Faloutsos},
title = {Net-Ray: Visualizing and Mining Billion-Scale Graphs},
booktitle = {Advances in Knowledge Discovery and Data Mining - 18th
Pacific-Asia Conference, {PAKDD} 2014, Tainan, Taiwan,
May 13-16, 2014. Proceedings, Part {I}},year  = {2014},
pages = {348--361},
}


@inproceedings{ LinRLKRF14,
author= {Yibin Lin and
Agha Ali Raza and
Jay Yoon Lee and
Danai Koutra and
Roni Rosenfeld and
Christos Faloutsos},
title = {Influence Propagation: Patterns, Model and a Case Study},
booktitle = {Advances in Knowledge Discovery and Data Mining - 18th
Pacific-Asia Conference, {PAKDD} 2014, Tainan, Taiwan,
May 13-16, 2014. Proceedings, Part {I}},
year  = {2014},
pages = {386--397},
}


@inproceedings{ AraujoPGFBSPK14,
author= {Miguel Araujo and
Spiros Papadimitriou and
Stephan G{\"{u}}nnemann and
Christos Faloutsos and
Prithwish Basu and
Ananthram Swami and
Evangelos E. Papalexakis and
Danai Koutra},
title = {Com2: Fast Automatic Discovery of Temporal ('Comet') 
Communities},
booktitle = {Advances in Knowledge Discovery and Data Mining - 18th 
Pacific-Asia Conference, {PAKDD} 2014, Tainan, Taiwan, 
May 13-16, 2014. Proceedings, Part {II}},
year  = {2014},
pages = {271--283},
}


@article{ KoutraBH14,
author= {Danai Koutra and
Paul N. Bennett and
Eric Horvitz},
title = {Events and Controversies: Influences of a Shocking News Event 
on Information Seeking},
journal   = {CoRR},
year  = {2014},
volume= {abs/1405.1486},
url   = {http://arxiv.org/abs/1405.1486},
}


@article{ AkogluTK14,
author= {Leman Akoglu and
Hanghang Tong and
Danai Koutra},
title = {Graph-based Anomaly Detection and Description: {A} Survey},
journal   = {Data Mining and Knowledge Discovery (DAMI)},
year  = {2014},
volume= {28},
number= {4},
publisher = {Springer}
}


@inproceedings{ KoutraKVF14,
author= {Danai Koutra and
U. Kang and
Jilles Vreeken and
Christos Faloutsos},
title = {{VOG:} Summarizing and Understanding Large Graphs},
booktitle = {Proceedings of the 2014 {SIAM} International Conference on Data 
Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014},
year  = {2014},
pages = {91--99},
}


@PhDThesis{ Koutra14,
author	= {Koutra, Danai},
title   = {{Large Graph Mining and Sense-making}},
school	= {Computer Science Department, Carnegie Mellon University},
type= {Master Thesis},
year= {2014}
}



@inproceedings{ KoutraTL13,
author= {Danai Koutra and
Hanghang Tong and
David Lubensky},
title = {{BIG-ALIGN:} Fast Bipartite Graph Alignment},
booktitle = {2013 {IEEE} 13th International Conference on Data Mining, 
Dallas, TX, USA, December 7-10, 2013},
year  = {2013},
pages = {389--398},
}


@inproceedings{ BerlingerioKEF13,
author= {Michele Berlingerio and
Danai Koutra and
Tina Eliassi{-}Rad and
Christos Faloutsos},
title = {Network similarity via multiple social theories},
booktitle = {Advances in Social Networks Analysis and Mining 2013, 
{ASONAM} '13, Niagara, ON, Canada - August 25 - 29, 2013},
year  = {2013},
pages = {1439--1440},
}


@inproceedings{ Senator+13,
author= {Ted E. Senator and
Henry G. Goldberg and
Alex Memory and
William T. Young and
Brad Rees and
Robert Pierce and
Daniel Huang and
Matthew Reardon and
David A. Bader and
Edmond Chow and
Irfan A. Essa and
Joshua Jones and
Vinay Bettadapura and
Duen Horng Chau and
Oded Green and
Oguz Kaya and
Anita Zakrzewska and
Erica Briscoe and
Rudolph L. Mappus IV and
Robert McColl and
Lora Weiss and
Thomas G. Dietterich and
Alan Fern and
Weng{-}Keen Wong and
Shubhomoy Das and
Andrew Emmott and
Jed Irvine and
Jay Yoon Lee and
Danai Koutra and
Christos Faloutsos and
Daniel D. Corkill and
Lisa Friedland and
Amanda Gentzel and
David Jensen},
title = {Detecting insider threats in a real corporate database of 
computer usage activity},
booktitle = {The 19th {ACM} {SIGKDD} International Conference on Knowledge 
Discovery and Data Mining, {KDD} 2013, Chicago, IL, USA, 
August 11-14, 2013},
year  = {2013},
pages = {1393--1401},
}


@InProceedings{ KoutraYSRJC13,
author	= {Danai Koutra and
Yu Gong and
Sephira Ryman and
Rex Jung and
Joshua T. Vogelstein and
Christos Faloutsos},
title		= {Are all brains wired equally?},
booktitle	= {Organization for Human Brain Mapping (OHBM)},
year		= {2013}
}


@inproceedings{LeeKKF13,
author= {Jay Yoon Lee and
U. Kang and
Danai Koutra and
Christos Faloutsos},
title = {Fast anomaly detection despite the duplicates},
booktitle = {Proceedings of the 22nd International Conference on World 
Wide Web (WWW Companion Volume)},
year  = {2013},
pages = {195-196},
}


@inproceedings {KoutraVF13,
author= {Koutra, Danai and 
Vogelstein, Joshua and 
Faloutsos, Christos},
title = {{DeltaCon: A Principled Massive-Graph Similarity Function}},
booktitle = {Proceedings of the 13th SIAM International Conference on 
Data Mining (SDM)},
year  = {2013},
pages = {162-170},
}


@inproceedings{ KoutraKPF13,
author= {Danai Koutra and 
Vasileios Koutras and
B. Aditya Prakash and
Christos Faloutsos},
title = {{Patterns amongst Competing Task Frequencies: Super-Linearities,
and the Almond-DG Model}},
booktitle = {Proceedings of the 17th Pacific-Asia Conference on Knowledge 
Discovery and Data Mining (PAKDD)},
year  = {2013},
pages = {201-212},
}


@inproceedings{ BerlingerioKEF12nips,
author   = "Berlingerio, Michele and Koutra, Danai and Eliassi-Rad,
Tina and Faloutsos, Christos",
title= {{A Scalable Approach to Size-Independent Network Similarity}},
booktitle= "NIPS 2012, Workshop on Social Network and Social Media Analysis, 
Methods, Models, and Applications, Lake Tahoe, NV, USA",
month= "Dec",
year = "2012",
}


@InProceedings{	KoutraPF12,
author	= {Danai Koutra and Evangelos Papalexakis and Christos
Faloutsos},
title		= {{TENSORSPLAT: Spotting Latent Anomalies in Time}},
booktitle	= {16th Panhellenic Conference on Informatics (PCI)},
year		= {2012}
}


@inproceedings{ BerlingerioKEF12,
author   = "Berlingerio, Michele and Koutra, Danai and Eliassi-Rad,
Tina and Faloutsos, Christos",
title= {{NetSimile: A Scalable Approach to Size-Independent Network
Similarity}},
booktitle   = "WIN 2012, Workshop on Information in Networks",
month= "Sept",
year = "2012",
}


@inproceedings{ HendersonGETBAKFL12,
author= {Keith Henderson and
Brian Gallagher and
Tina Eliassi-Rad and
Hanghang Tong and
Sugato Basu and
Leman Akoglu and
Danai Koutra and
Christos Faloutsos and
Lei Li},
title = {{RolX: structural role extraction {\&} mining in large graphs}},
booktitle = {Proceedings of the 18th ACM International Conference on 
Knowledge Discovery and Data Mining (SIGKDD)},
year  = {2012},
pages = {1231-1239},
}


@inproceedings{ AkogluCKKF12,
author = {Akoglu, Leman and Chau, Duen Horng and Kang, U.
and Koutra, Danai and Faloutsos, Christos},
title = {{OPAvion: mining and visualization in large graphs}},
series = {Proceedings of the ACM International Conference on Management 
of Data (SIGMOD)},
year = {2012},
pages = {717--720},
publisher = {ACM},
}


@incollection {KoutraKKCPF11,
author = {Koutra, Danai and Ke, Tai-You and Kang, U. and 
Chau, Duen and Pao, Hsing-Kuo and Faloutsos, Christos},
title  = {{Unifying Guilt-by-Association Approaches:
Theorems and Fast Algorithms}},
booktitle = {Machine Learning and Knowledge Discovery in Databases 
(ECML/PKDD)},
series = {Lecture Notes in Computer Science},
pages  = {245-260},
volume = {6912},
year   = {2011}
}


@PhDThesis{ Koutra10,
author	= {Koutra, Danai},
title   = {{Approximate sequence matching with MapReduce}},
school	= {Electrical and Computer Engineering, 
National Technical University of Athens},
type= {Diploma Thesis},
year= {2010}
}

Overview Schedule Assignments Grading Resources

EECS 598: Special Topics, Fall 2015
Graph Mining and Exploration at Scale:
Methods and Applications

wiki-graph

Graphs naturally represent information ranging from links between webpages to friendships in social networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we extract useful knowledge, understand the underlying processes, and make interesting discoveries?

This course will cover recent methods and algorithms for exploring and analyzing large graphs, as well as applications in various domains (e.g., web, social science, computer networks, neuroscience). The focus will be on scalable and practical methods, and the students will have the chance to analyze large-scale datasets. The topics that we will cover include ranking, label propagation, clustering and community detection, summarization, similarity, and anomaly detection in the graph setting.

Objectives: This course aims to introduce students to graph mining. Students will become familiar with the challenges of processing large amounts of data, state-of-the-art methods and algorithms for analyzing graphs, and applications of graph mining in various domains. We expect that by the end of the course, students will have a thorough understanding of the graph mining foundations, and will be able to critique graph mining methods, formulate and solve graph-related problems, and analyze large-scale datasets.

Prerequisites: Students are expected to (1) have basic knowledge of linear algebra, (2) be familiar with probability theory/statistics, and (3) have good programming skills (e.g., Python, JAVA, Matlab, R, or any programming language of their preference).
*** Advanced-standing undergraduates or students who do not meet the prerequisites may enroll with permission of the instructor.

Instructor: Danai Koutra
Office Hours: Thu 12:30-1:30pm @ BBB 4824
E-mail: dkoutra@umich.edu

Lectures & Discussion:
When? Tue/Thu 10:30am-12:30pm
Where? EECS 3427


Schedule (tentative)
[top]

!! The topics and dates of the lectures are subject to change. The following schedule outlines the topics that we will be covering in this course.



Readings

Static graphs: laws and patterns

Dynamic graphs: laws and patterns

Random Walks, Pagerank, HITS

In class:

Other readings:

Node Classification: Belief Propagation

In class:

Other readings:

Node Classification: Semi-Supervised Learning

In class:

Other readings:

Node Similarity

In class:

Other readings:

Graph Similarity

In class:

Other readings:

Graph Alignment

In class:

  • *** Arvind Narayanan and Vitaly Shmatikov. 2009. De-anonymizing Social Networks. In Proceedings of the 2009 30th IEEE Symposium on Security and Privacy (SP '09). IEEE Computer Society, Washington, DC, USA, 173-187.
  • ** Danai Koutra, Hanghang Tong, and David Lubensky. Big-Align: Fast Bipartite Graph Alignment. In Proceedings of the 14th IEEE International Conference on Data Mining (ICDM), Dallas, Texas, 2013.

Other readings:

Graph Clustering and Communities

In class:

Other readings:

Graph Summarization

In class:

Other readings:

Anomaly Detection

In class:

Other readings:

Link Analysis

In class:

Other readings:

Streaming graphs and algorithms

In class:

Other readings:

Deep learning for graphs

In class:

Recommendation Systems

In class:

Other readings:

Large-scale social science

In class:

Other readings:


Assignments
[top]

The coursework will consist of some short, practical assignments that will familiarize the students with the challenges of large-scale graph analysis, as well as a semester-long project (related to topics discussed in class) that will be selected by students. For more information, look out for the announcements on Canvas.


Grading
[top]

Class Participation: 30%
Short Assignments: 20%
Project: 50%

Resources
[top]

Check the class on Canvas to see pointers to datasets, code, and tools that will be useful for your projects, and pratical assignments.

Announcements
* News
Oct 18, 2015: Pay attention to the announcements on Canvas!
Oct 5, 2015: The survey is due on October 11, at 9pm.
Sept 19, 2015: The first assingment is out, and you can find it on Canvas. It's due on October 2nd (at 5pm).
Sept 13, 2015: The class is now published on Canvas. If you are registered, you should be able to see it in the Wolverine Access Student Center. You will find useful resources for your projects (data, tools, code). Start brainstorming!
Aug 30, 2015: First class will be on Tuesday, September 8th.


Copyright © Danai Koutra

Last Modified on 08/30/2015.