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110
Graph Distances in the Streaming Model: The Value of Space * Joan Feigenbaum ## # Sampath Kannan $  Andrew McGregor $k Siddharth Suri $**
"... Abstract We investigate the importance of space when solvingproblems based on graph distance in the streaming model. In this model, the input graph is presentedas a stream of edges in an arbitrary order. The main computational restriction of the model is that wehave limited space and therefore canno ..."
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Abstract We investigate the importance of space when solvingproblems based on graph distance in the streaming model. In this model, the input graph is presentedas a stream of edges in an arbitrary order. The main computational restriction of the model is that wehave limited space and therefore cannot store all the streamed data; we are forced to make spaceefficientsummaries of the data as we go along. For a graph of nvertices and m edges, we show that testing many graphproperties, including connectivity (ergo any reasonable decision problem about distances) and bipartiteness,requires \Omega ( n) bits of space. Given this, we theninvestigate how the power of the model increases as we relax our space restriction. Our main result isan efficient randomized algorithm that constructs a (2t + 1)spanner in one pass. With high probability,it uses O(t * n1+1/t log2 n) bits of space and processeseach edge in the stream in O(t2 * n1/t log n) time.We find approximations to diameter and girth via the constructed spanner. For t = \Omega ( log nlog log n), the space requirement of the algorithm is O(n*polylog n), and theperedge processing time is O(polylog n). We also showa corresponding lower bound of t for the approximationratio achievable when the space restriction is O(t * n1+1/t log2 n).We then consider the scenario in which we are allowed multiple passes over the input stream. Here,we investigate whether allowing these extra passes will compensate for a given space restriction. We show that
ENHANCING FINANCIAL SECTOR SURVEILLANCE IN LOW INCOME COUNTRIES (LICS)—CASE STUDIES Approved By Siddharth Tiwari,
"... Prepared by an interdepartmental team led by Mary Zephirin (MCM) and Ritu ..."
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Prepared by an interdepartmental team led by Mary Zephirin (MCM) and Ritu
Feedback Effects between Similarity and Social Influence in Online Communities
"... A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second ..."
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Cited by 162 (8 self)
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A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings. We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction — but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know
Conducting behavioral research on Amazon’s Mechanical Turk. Behav Res Methods 2012;44(1):1–23
"... Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this paper is to demonstrate how to use this website for conducting behavioral research and lower the barrier to entry for researchers who could benefit ..."
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Cited by 123 (5 self)
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Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this paper is to demonstrate how to use this website for conducting behavioral research and lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments and faster iteration between developing theory and executing experiments. We will discuss how the behavior of workers compares to experts and to laboratory subjects. Then, we illustrate the mechanics of putting a task on Mechanical Turk including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform including techniques for conducting synchronous experiments, methods to ensure high quality work, how to keep data private, and how to maintain code security.
On graph problems in a semistreaming model
 In 31st International Colloquium on Automata, Languages and Programming
, 2004
"... Abstract. We formalize a potentially rich new streaming model, the semistreaming model, that we believe is necessary for the fruitful study of efficient algorithms for solving problems on massive graphs whose edge sets cannot be stored in memory. In this model, the input graph, G = (V, E), is prese ..."
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Cited by 108 (16 self)
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Abstract. We formalize a potentially rich new streaming model, the semistreaming model, that we believe is necessary for the fruitful study of efficient algorithms for solving problems on massive graphs whose edge sets cannot be stored in memory. In this model, the input graph, G = (V, E), is presented as a stream of edges (in adversarial order), and the storage space of an algorithm is bounded by O(n · polylog n), where n = V . We are particularly interested in algorithms that use only one pass over the input, but, for problems where this is provably insufficient, we also look at algorithms using constant or, in some cases, logarithmically many passes. In the course of this general study, we give semistreaming constant approximation algorithms for the unweighted and weighted matching problems, along with a further algorithm improvement for the bipartite case. We also exhibit log n / log log n semistreaming approximations to the diameter and the problem of computing the distance between specified vertices in a weighted graph. These are complemented by Ω(log (1−ɛ) n) lower bounds. 1
A Model of Computation for MapReduce
 Proc. ACMSIAM SODA
, 2010
"... In recent years the MapReduce framework has emerged as one of the most widely used parallel computing platforms for processing data on terabyte and petabyte scales. Used daily at companies such as Yahoo!, Google, Amazon, and Facebook, and adopted more recently by several universities, it allows for ..."
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Cited by 102 (7 self)
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In recent years the MapReduce framework has emerged as one of the most widely used parallel computing platforms for processing data on terabyte and petabyte scales. Used daily at companies such as Yahoo!, Google, Amazon, and Facebook, and adopted more recently by several universities, it allows for easy parallelization of data intensive computations over many machines. One key feature of MapReduce that differentiates it from previous models of parallel computation is that it interleaves sequential and parallel computation. We propose a model of efficient computation using the MapReduce paradigm. Since MapReduce is designed for computations over massive data sets, our model limits the number of machines and the memory per machine to be substantially sublinear in the size of the input. On the other hand, we place very loose restrictions on the computational power of of any individual machine— our model allows each machine to perform sequential computations in time polynomial in the size of the original input. We compare MapReduce to the PRAM model of computation. We prove a simulation lemma showing that a large class of PRAM algorithms can be efficiently simulated via MapReduce. The strength of MapReduce, however, lies in the fact that it uses both sequential and parallel computation. We demonstrate how algorithms can take advantage of this fact to compute an MST of a dense graph in only two rounds, as opposed to Ω(log(n)) rounds needed in the standard PRAM model. We show how to evaluate a wide class of functions using the MapReduce framework. We conclude by applying this result to show how to compute some basic algorithmic problems such as undirected st connectivity in the MapReduce framework. 1
An experimental study on the coloring problem on human subject networks
 30/06/2008 15:45Gero Schwenk and Torsten Reimer: Simple Heuristics in Complex Networks Page 17 of 18http://jasss.soc.surrey.ac.uk/11/3/4.html KERR, N and Tindale, R
, 2006
"... Theoretical work suggests that structural properties of naturally occurring networks are important in shaping behavior and dynamics. However, the relationships between structure and behavior are difficult to establish through empirical studies, because the networks in such studies are typically fixe ..."
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Cited by 80 (12 self)
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Theoretical work suggests that structural properties of naturally occurring networks are important in shaping behavior and dynamics. However, the relationships between structure and behavior are difficult to establish through empirical studies, because the networks in such studies are typically fixed. We studied networks of human subjects attempting to solve the graph or network coloring problem, which models settings in which it is desirable to distinguish one’s behavior from that of one’s network neighbors. Networks generated by preferential attachment made solving the coloring problem more difficult than did networks based on cyclical structures, and ‘‘small worlds’ ’ networks were easier still. We also showed that providing more information can have opposite effects on performance, depending on network structure. I t is often thought that structural properties of naturally occurring networks are influential in shaping individual and collective behavior and dynamics. Examples include the popular notion that Bhubs [ or Bconnectors [ are inordi
Graph distances in the streaming model: the value of space
 In ACMSIAM Symposium on Discrete Algorithms
, 2005
"... We investigate the importance of space when solving problems based on graph distance in the streaming model. In this model, the input graph is presented as a stream of edges in an arbitrary order. The main computational restriction of the model is that we have limited space and therefore cannot stor ..."
Abstract

Cited by 69 (11 self)
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We investigate the importance of space when solving problems based on graph distance in the streaming model. In this model, the input graph is presented as a stream of edges in an arbitrary order. The main computational restriction of the model is that we have limited space and therefore cannot store all the streamed data; we are forced to make spaceefficient summaries of the data as we go along. For a graph of n vertices and m edges, we show that testing many graph properties, including connectivity (ergo any reasonable decision problem about distances) and bipartiteness, requires Ω(n) bits of space. Given this, we then investigate how the power of the model increases as we relax our space restriction. Our main result is an efficient randomized algorithm that constructs a (2t + 1)spanner in one pass. With high probability, it uses O(t · n 1+1/t log 2 n) bits of space and processes each edge in the stream in O(t 2 · n 1/t log n) time. We find approximations to diameter and girth via the log n constructed spanner. For t = Ω (), the space log log n requirement of the algorithm is O(n·polylog n), and the peredge processing time is O(polylog n). We also show a corresponding lower bound of t for the approximation ratio achievable when the space restriction is O(t · n1+1/t log 2 n). We then consider the scenario in which we are allowed multiple passes over the input stream. Here, we investigate whether allowing these extra passes will compensate for a given space restriction. We show that ∗This work was supported by the DoD University Research Initiative (URI) administered by the Office of Naval Research
Cooperation and contagion in webbased, networked public goods experiments
 PLoS ONE
, 2011
"... A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in ..."
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Cited by 23 (4 self)
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A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of experiments on Amazon Mechanical Turk, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial “seed ” players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation did not spread beyond direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and costeffectiveness of webbased experiments over those conducted in physical labs.
Results 1  10
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