Improved Theoretical and Practical Guarantees for Chromatic Correlation Clustering

We study a natural generalization of the correlation cluster-ing problem to graphs in which the pairwise relations be-tween objects are categorical instead of binary. This prob-lem was recently introduced by Bonchi et al. under the name of chromatic correlation clustering, and is motivated by many real-world applications in data-mining and social networks, including community detection, link classification, and entity de-duplication. Our main contribution is a fast and easy-to-implement constant approximation framework for the problem, which builds on a novel reduction of the problem to that of cor-relation clustering. This result significantly progresses the current state of knowledge for the problem, improving on a previous result that only guaranteed linear approximation in the input size. We complement the above result by devel-oping a linear programming-based algorithm that achieves an improved approximation ratio of 4. Although this al-gorithm cannot be considered to be practical, it further ex-tends our theoretical understanding of chromatic correlation clustering. We also present a fast heuristic algorithm that is motivated by real-life scenarios in which there is a ground-truth clustering that is obscured by noisy observations. We test our algorithms on both synthetic and real datasets, like social networks data. Our experiments reinforce the theoret-ical findings by demonstrating that our algorithms generally outperform previous approaches, both in terms of solution cost and reconstruction of an underlying ground-truth clus-tering

Overlapping and Robust Edge-Colored Clustering in Hypergraphs

A recent trend in data mining has explored (hyper)graph clustering algorithms for data with categorical relationship types. Such algorithms have applications in the analysis of social, co-authorship, and protein interaction networks, to name a few. Many such applications naturally have some overlap between clusters, a nuance which is missing from current combinatorial models. Additionally, existing models lack a mechanism for handling noise in datasets. We address these concerns by generalizing Edge-Colored Clustering, a recent framework for categorical clustering of hypergraphs. Our generalizations allow for a budgeted number of either (a) overlapping cluster assignments or (b) node deletions. For each new model we present a greedy algorithm which approximately minimizes an edge mistake objective, as well as bicriteria approximations where the second approximation factor is on the budget. Additionally, we address the parameterized complexity of each problem, providing FPT algorithms and hardness results

On the External Validity of Average-Case Analyses of Graph Algorithms

The number one criticism of average-case analysis is that we do not actually know the probability distribution of real-world inputs. Thus, analyzing an algorithm on some random model has no implications for practical performance. At its core, this criticism doubts the existence of external validity, i.e., it assumes that algorithmic behavior on the somewhat simple and clean models does not translate beyond the models to practical performance real-world input. With this paper, we provide a first step towards studying the question of external validity systematically. To this end, we evaluate the performance of six graph algorithms on a collection of 2745 sparse real-world networks depending on two properties; the heterogeneity (variance in the degree distribution) and locality (tendency of edges to connect vertices that are already close). We compare this with the performance on generated networks with varying locality and heterogeneity. We find that the performance in the idealized setting of network models translates surprisingly well to real-world networks. Moreover, heterogeneity and locality appear to be the core properties impacting the performance of many graph algorithms.Comment: 42 pages, 19 figures, preprint (full version

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Deep learning systems as complex networks

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks, can discover intricate statistical structure in large data sets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article we propose to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.Comment: 20 pages, 9 figure

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

NCUWM Talk Abstracts 2010

Dr. Bryna Kra, Northwestern University “From Ramsey Theory to Dynamical Systems and Back” Dr. Karen Vogtmann, Cornell University “Ping-Pong in Outer Space” Lindsay Baun, College of St. Benedict Danica Belanus, University of North Dakota Hayley Belli, University of Oregon Tiffany Bradford, Saint Francis University Kathryn Bryant, Northern Arizona University Laura Buggy, College of St. Benedict Katharina Carella, Ithaca College Kathleen Carroll, Wheaton College Elizabeth Collins-Wildman, Carleton College Rebecca Dorff, Brigham Young University Melisa Emory, University of Nebraska at Omaha Avis Foster, George Mason University Xiaojing Fu, Clarkson University Jennifer Garbett, Kenyon College Nicki Gaswick, University of Nebraska-Lincoln Rita Gnizak, Fort Hays State University Kailee Gray, University of South Dakota Samantha Hilker, Sam Houston State University Ruthi Hortsch, University of Michigan Jennifer Iglesias, Harvey Mudd College Laura Janssen, University of Nebraska-Lincoln Laney Kuenzel, Stanford University Ellen Le, Pomona College Thu Le, University of the South Shauna Leonard, Arkansas State University Tova Lindberg, Bethany Lutheran College Lisa Moats, Concordia College Kaitlyn McConville, Westminster College Jillian Neeley, Ithaca College Marlene Ouayoro, George Mason University Kelsey Quarton, Bradley University Brooke Quisenberry, Hope College Hannah Ross, Kenyon College Karla Schommer, College of St. Benedict Rebecca Scofield, University of Iowa April Scudere, Westminster College Natalie Sheils, Seattle University Kaitlin Speer, Baylor University Meredith Stevenson, Murray State University Kiri Sunde, University of North Carolina Kaylee Sutton, John Carroll University Frances Tirado, University of Florida Anna Tracy, University of the South Kelsey Uherka, Morningside College Danielle Wheeler, Coe College Lindsay Willett, Grove City College Heather Williamson, Rice University Chengcheng Yang, Rice University Jie Zeng, Michigan Technological Universit

The Maximum Clique Problem: Algorithms, Applications, and Implementations

Computationally hard problems are routinely encountered during the course of solving practical problems. This is commonly dealt with by settling for less than optimal solutions, through the use of heuristics or approximation algorithms. This dissertation examines the alternate possibility of solving such problems exactly, through a detailed study of one particular problem, the maximum clique problem. It discusses algorithms, implementations, and the application of maximum clique results to real-world problems. First, the theoretical roots of the algorithmic method employed are discussed. Then a practical approach is described, which separates out important algorithmic decisions so that the algorithm can be easily tuned for different types of input data. This general and modifiable approach is also meant as a tool for research so that different strategies can easily be tried for different situations. Next, a specific implementation is described. The program is tuned, by use of experiments, to work best for two different graph types, real-world biological data and a suite of synthetic graphs. A parallel implementation is then briefly discussed and tested. After considering implementation, an example of applying these clique-finding tools to a specific case of real-world biological data is presented. Results are analyzed using both statistical and biological metrics. Then the development of practical algorithms based on clique-finding tools is explored in greater detail. New algorithms are introduced and preliminary experiments are performed. Next, some relaxations of clique are discussed along with the possibility of developing new practical algorithms from these variations. Finally, conclusions and future research directions are given