Contacts and influence

Pool, Ithiel de Sola; Kochen, Manfred

doi:10.1016/0378-8733(78)90011-4

Cited by 439 publications

(124 citation statements)

References 38 publications

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“…Social networks have been documented in the sociological literature, but are generally inappropriate for epidemiological purposes. Definitions of contacts that do not correlate closely with transmission opportunities, such as relationship-based definitions, inclusion of remote (letter, telephone or e-mail) interactions or the measurement of a particular subset of social contacts, render many such studies unsuitable from the epidemiological perspective (de Sola Pool & Kochen 1978;Bernard et al 1990;Wasserman & Faust 1994;Dunbar & Spoors 1995;Beutels et al 2006). As a consequence, there is relatively little available information about the patterns of human social interactions relevant to the transmission of many infectious diseases (Edmunds et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Dynamic social networks and the implications for the spread of infectious disease

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Eames

Edmunds³

2008

J. R. Soc. Interface.

326

382

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Understanding the nature of human contact patterns is crucial for predicting the impact of future pandemics and devising effective control measures. However, few studies provide a quantitative description of the aspects of social interactions that are most relevant to disease transmission. Here, we present the results from a detailed diary-based survey of casual (conversational) and close contact (physical) encounters made by a small peer group of 49 adults who recorded 8661 encounters with 3528 different individuals over 14 non-consecutive days. We find that the stability of interactions depends on the intimacy of contact and social context. Casual contact encounters mostly occur in the workplace and are predominantly irregular, while close contact encounters mostly occur at home or in social situations and tend to be more stable. Simulated epidemics of casual contact transmission involve a large number of non-repeated encounters, and the social network is well captured by a random mixing model. However, the stability of the social network should be taken into account for close contact infections. Our findings have implications for the modelling of human epidemics and planning pandemic control policies based on social distancing methods.

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Section: Introductionmentioning

confidence: 99%

Dynamic social networks and the implications for the spread of infectious disease

Read

Eames

Edmunds³

2008

J. R. Soc. Interface.

326

382

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“…This suggests that S3G2 can generate extremely large graphs quickly on a Hadoop cluster with large resources. There is a lot of work studying the characteristics of social networks [11,7,12,15,5,1,9] and also on the generation of random graphs having global properties similar to a social network [14,3,4,10,6,8]. However, to the best of our knowledge, there is no generator that creates a synthetic social graph with correlations.…”

Section: Case Study: Generating Social Network Datamentioning

confidence: 99%

S3G2: A Scalable Structure-Correlated Social Graph Generator

Pham

Boncz

Erling

2013

Lecture Notes in Computer Science

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Abstract. Benchmarking graph-oriented database workloads and graph-oriented database systems is increasingly becoming relevant in analytical Big Data tasks, such as social network analysis. In graph data, structure is not mainly found inside the nodes, but especially in the way nodes happen to be connected, i.e. structural correlations. Because such structural correlations determine join fan-outs experienced by graph analysis algorithms and graph query executors, they are an essential, yet typically neglected, ingredient of synthetic graph generators. To address this, we present S3G2: a Scalable Structure-correlated Social Graph Generator. This graph generator creates a synthetic social graph, containing non-uniform value distributions and structural correlations, which is intended as test data for scalable graph analysis algorithms and graph database systems. We generalize the problem to decompose correlated graph generation in multiple passes that each focus on one so-called correlation dimension; each of which can be mapped to a MapReduce task. We show that S3G2 can generate social graphs that (i) share well-known graph connectivity characteristics typically found in real social graphs (ii) contain certain plausible structural correlations that influence the performance of graph analysis algorithms and queries, and (iii) can be quickly generated at huge sizes on common cluster hardware.Data in real life is correlated; e.g. people living in Germany have a different distribution in names than people in Italy (location), and people who went to the same university in the same period have a much higher probability to be friends in a social network. Such correlations can strongly influence the intermediate result sizes of query plans, the effectiveness of indexing strategies, and cause absence or presence of locality in data access patterns. Regarding intermediate result sizes of selections, consider:SELECT personID FROM person WHERE firstName = 'Joachim' AND addressCountry = 'Germany' Query optimizers commonly use the independence assumption for estimating the result size of conjunctive predicates, by multiplying the estimates for the individual predicates. This would underestimate this result size, since Joachim is more common in Germany than in most other countries; similar would happen e.g. when querying for firstName 'Cesare' from 'Italy'. Overestimation can also easily happen, if we would query for 'Cesare' from 'Germany' or 'Joachim' from 'Italy ' (i.e. anti-correlation).This correlation problem has been recognized in relational database systems as relevant, and some work exists to detect correlated properties inside the same table (e.g., see [13]). Still, employing techniques for the detection of correlation is hardly mainstream in relational database management, and this is even more so when we start considering correlations between predicates that are separated by joins. Consider for instance the DBLP example of co-authorship of papers that counts the number of authors that have published both in TODS and...

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“…This is a fact that we exploit in our application to determine changes in dynamic (peer-to-peer) networks. The reader might note the resemblance with the classical Laplace (or more precisely Laplace-Beltrami) flow that has become, by now, standard in Imaging and Graphics (see, e.g., [37,42] and the references therein), defined as…”

Section: Ricci-flow With Forman Curvaturementioning

confidence: 99%

“…The motivation behind this choice of weights lies in the "small world"-property (i.e., a maximum degree of separation of six [37]) that has been reportedly found in real-world. In accounting for this, we only check for indirect connections up to a path length of six and scale the weights according to the distribution.…”

Section: Characterizing Large Data Sets With Ricci Curvaturementioning

confidence: 99%

Forman-Ricci Flow for Change Detection in Large Dynamic Data Sets

Weber

Jost

Saucan

2016

Axioms

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Abstract:We present a viable geometric solution for the detection of dynamic effects in complex networks. Building on Forman's discretization of the classical notion of Ricci curvature, we introduce a novel geometric method to characterize different types of real-world networks with an emphasis on peer-to-peer networks. We study the classical Ricci-flow in a network-theoretic setting and introduce an analytic tool for characterizing dynamic effects. The formalism suggests a computational method for change detection and the identification of fast evolving network regions and yields insights into topological properties and the structure of the underlying data.

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Contacts and influence

Cited by 439 publications

References 38 publications

Dynamic social networks and the implications for the spread of infectious disease

Dynamic social networks and the implications for the spread of infectious disease

S3G2: A Scalable Structure-Correlated Social Graph Generator

Forman-Ricci Flow for Change Detection in Large Dynamic Data Sets

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