Parallel Bayesian Methods for Community Detection
Abstract not provided.
Publications
CHALLENGES IN PARALLEL GRAPH PROCESSING
Parallel Processing Letters
Challenges in Streaming Analysis
Abstract not provided.
Challenges in Streaming Graph Analysis
Abstract not provided.
Sensor placement for municipal water networks
We consider the problem of placing a limited number of sensors in a municipal water distribution network to minimize the impact over a given suite of contamination incidents. In its simplest form, the sensor placement problem is a p-median problem that has structure extremely amenable to exact and heuristic solution methods. We describe the solution of real-world instances using integer programming or local search or a Lagrangian method. The Lagrangian method is necessary for solution of large problems on small PCs. We summarize a number of other heuristic methods for effectively addressing issues such as sensor failures, tuning sensors based on local water quality variability, and problem size/approximation quality tradeoffs. These algorithms are incorporated into the TEVA-SPOT toolkit, a software suite that the US Environmental Protection Agency has used and is using to design contamination warning systems for US municipal water systems.
Accelerating multicore graph algorithms by trading latency for bandwidth
Abstract not provided.
Listing triangles in expected linear time on a class of power law graphs
Enumerating triangles (3-cycles) in graphs is a kernel operation for social network analysis. For example, many community detection methods depend upon finding common neighbors of two related entities. We consider Cohen's simple and elegant solution for listing triangles: give each node a 'bucket.' Place each edge into the bucket of its endpoint of lowest degree, breaking ties consistently. Each node then checks each pair of edges in its bucket, testing for the adjacency that would complete that triangle. Cohen presents an informal argument that his algorithm should run well on real graphs. We formalize this argument by providing an analysis for the expected running time on a class of random graphs, including power law graphs. We consider a rigorously defined method for generating a random simple graph, the erased configuration model (ECM). In the ECM each node draws a degree independently from a marginal degree distribution, endpoints pair randomly, and we erase self loops and multiedges. If the marginal degree distribution has a finite second moment, it follows immediately that Cohen's algorithm runs in expected linear time. Furthermore, it can still run in expected linear time even when the degree distribution has such a heavy tail that the second moment is not finite. We prove that Cohen's algorithm runs in expected linear time when the marginal degree distribution has finite 4/3 moment and no vertex has degree larger than {radical}n. In fact we give the precise asymptotic value of the expected number of edge pairs per bucket. A finite 4/3 moment is required; if it is unbounded, then so is the number of pairs. The marginal degree distribution of a power law graph has bounded 4/3 moment when its exponent {alpha} is more than 7/3. Thus for this class of power law graphs, with degree at most {radical}n, Cohen's algorithm runs in expected linear time. This is precisely the value of {alpha} for which the clustering coefficient tends to zero asymptotically, and it is in the range that is relevant for the degree distribution of the World-Wide Web.
Comparing Programming Paradigms for Graph Algorithms
Abstract not provided.
Implementing a portable multi-threaded graph library: The mtgl on qthreads
IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium
Abstract not provided.
Low-memory Lagrangian relaxation methods for sensor placement in municipal water networks
World Environmental and Water Resources Congress 2008: Ahupua'a - Proceedings of the World Environmental and Water Resources Congress 2008
Placing sensors in municipal water networks to protect against a set of contamination events is a classic p-median problem for most objectives when we assume that sensors are perfect. Many researchers have proposed exact and approximate solution methods for this p-median formulation. For full-scale networks with large contamination event suites, one must generally rely on heuristic methods to generate solutions. These heuristics provide feasible solutions, but give no quality guarantee relative to the optimal placement. In this paper we apply a Lagrangian relaxation method in order to compute lower bounds on the expected impact of suites of contamination events. In all of our experiments with single objectives, these lower bounds establish that the GRASP local search method generates solutions that are provably optimal to to within a fraction of a percentage point. Our Lagrangian heuristic also provides good solutions itself and requires only a fraction of the memory of GRASP. We conclude by describing two variations of the Lagrangian heuristic: an aggregated version that trades off solution quality for further memory savings, and a multi-objective version which balances objectives with additional goals. © 2008 ASCE.
Tolerating the community detection resolution limit with edge weighting
Proposed for publication in the Proceedings of the National Academy of Sciences.
Communities of vertices within a giant network such as the World-Wide-Web are likely to be vastly smaller than the network itself. However, Fortunato and Barthelemy have proved that modularity maximization algorithms for community detection may fail to resolve communities with fewer than {radical} L/2 edges, where L is the number of edges in the entire network. This resolution limit leads modularity maximization algorithms to have notoriously poor accuracy on many real networks. Fortunato and Barthelemy's argument can be extended to networks with weighted edges as well, and we derive this corollary argument. We conclude that weighted modularity algorithms may fail to resolve communities with fewer than {radical} W{epsilon}/2 total edge weight, where W is the total edge weight in the network and {epsilon} is the maximum weight of an inter-community edge. If {epsilon} is small, then small communities can be resolved. Given a weighted or unweighted network, we describe how to derive new edge weights in order to achieve a low {epsilon}, we modify the 'CNM' community detection algorithm to maximize weighted modularity, and show that the resulting algorithm has greatly improved accuracy. In experiments with an emerging community standard benchmark, we find that our simple CNM variant is competitive with the most accurate community detection methods yet proposed.
The TEVA-SPOT toolkit for drinking water contaminant warning system design
World Environmental and Water Resources Congress 2008: Ahupua'a - Proceedings of the World Environmental and Water Resources Congress 2008
We present the TEVA-SPOT Toolkit, a sensor placement optimization tool developed within the USEPA TEVA program. The TEVA-SPOT Toolkit provides a sensor placement framework that facilitates research in sensor placement optimization and enables the practical application of sensor placement solvers to real-world CWS design applications. This paper provides an overview of its key features, and then illustrates how this tool can be flexibly applied to solve a variety of different types of sensor placement problems. © 2008 ASCE.
Limited-memory techniques for sensor placement in water distribution networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
The practical utility of optimization technologies is often impacted by factors that reflect how these tools are used in practice, including whether various real-world constraints can be adequately modeled, the sophistication of the analysts applying the optimizer, and related environmental factors (e.g. whether a company is willing to trust predictions from computational models). Other features are less appreciated, but of equal importance in terms of dictating the successful use of optimization. These include the scale of problem instances, which in practice drives the development of approximate solution techniques, and constraints imposed by the target computing platforms. End-users often lack state-of-the-art computers, and thus runtime and memory limitations are often a significant, limiting factor in algorithm design. When coupled with large problem scale, the result is a significant technological challenge. We describe our experience developing and deploying both exact and heuristic algorithms for placing sensors in water distribution networks to mitigate against damage due intentional or accidental introduction of contaminants. The target computing platforms for this application have motivated limited-memory techniques that can optimize large-scale sensor placement problems. © 2008 Springer Berlin Heidelberg.
The PageRank Derby
Abstract not provided.
U. S. Environmental Protection Agency uses Operations Research to Reduce Drinking Water Contamination Risks
INFORMS Interfaces Journal
Abstract not provided.
Informatics Architectures
Abstract not provided.
Cray XMT Brings New Energy to High-Performance Computing
SciDAC Review
Abstract not provided.
Graph Analysis with High-Performance Computing
Abstract not provided.
The MultiThreaded Graph Library
Abstract not provided.
Nightly Software Testing in Acro with EXACT and FAST
Abstract not provided.
Designing Contamination Warning System for Municipal Water Networks Using Imperfect Sensors
Journal of Water Resources Planning and Management
Abstract not provided.
Sandia/EPA TEVA-SPOT Water Security Team Chosen as Finalist for 2008 Edelman Award Competition
Abstract not provided.
Scheduling manual sampling for contamination detection in municipal water networks
8th Annual Water Distribution Systems Analysis Symposium 2006
Cities without an early warning system of indwelling sensors can consider monitoring their networks manually, especially during times of heightened security levels. We consider the problem of calculating an optimal schedule for manual sampling in a municipal water network. Preliminary computations with a small-scale example indicate that during normal times, manual sampling can provide some benefit, but it is far inferior to an indwelling sensor network. However, given information that significantly constrains the nature of an imminent threat, manual sampling can perform as well as a small sensor network designed to handle normal threats. Copyright ASCE 2006.
Limited-Memory Techniques for Sensor Placement in Water Distribution Networks
Abstract not provided.
The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms
Journal of Water Resources, Planning and Management
Abstract not provided.
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