Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

How change agents and social capital influence the adoption of innovations among small farmers: Evidence from social networks in rural Bolivia

"This paper presents results from a study that identified patterns of social interaction among small farmers in three agricultural subsectors in Bolivia—fish culture, peanut production, and quinoa production—and analyzed how social interaction influences farmers' behavior toward the adoption of pro-poor innovations. Twelve microregions were identified, four in each subsector, setting the terrain for an analysis of parts of social networks that deal with the diffusion of specific sets of innovations. Three hundred sixty farmers involved in theses networks as well as 60 change agents and other actors promoting directly or indirectly the diffusion of innovations were interviewed about the interactions they maintain with other agents in the network and the sociodemographic characteristics that influence their adoption behavior. The information derived from this data collection was used to test a wide range of hypotheses on the impact that the embeddedness of farmers in social networks has on the intensity with which they adopt innovations. Evidence provided by the study suggests that persuasion, social influence, and competition are significant influences in the decisions of farmers in poor rural regions in Bolivia to adopt innovations. The results of this study are meant to attract the attention of policymakers and practitioners who are interested in the design and implementation of projects and programs fostering agricultural innovation and who may want to take into account the effects of social interaction and social capital. Meanwhile, scholars of the diffusion of innovations may find evidence to further embrace the complexity and interdependence of social interactions in their models and approaches." from Author's AbstractSocial networks, Agricultural innovation, Change agent, Social capital,

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

Social networks and economic life in rural Zambia

This thesis explores the relationship between social networks and economic life in rural Zambia. The motivation for the study lies in the crucial role played by social context and social networks in exchange behaviour in rural sub-Saharan Africa, and inherent difficulties in formalising market transactions in this context within a standard neoclassical economics framework. The study examines the role of social networks in rural production systems, focusing on crop market participation. It is based on analysis of findings from social network research conducted by the author in three predominantly Bemba villages in Northern Province, Zambia. Data collected using quantitative and qualitative methods are used to map social networks of individuals and households. Variables are constructed capturing network characteristics, and incorporated into transactions cost models of ommercialisation. The overarching question is: do social networks play a role in determining farming success in settings with little variability between households on assets and endowments – land, labour, inputs – and where markets are incomplete or missing? Do social networks mediate market and resource access, helping to explain socio-economic differences between households? The research finds rural life is characterised by diverse networks with multiple, overlapping functions. Much economic exchange takes place on reciprocal or kinship bases, rooted in social norms and reflecting community structures. How social networks are measured matters. Different network attributes are important for different people, and relationships between networks and outcomes depend on the measure used. Controlling for endogeneity, estimation results suggest larger networks have a negative effect on crop incomes whereas having a greater proportion of kin in the network has a positive effect, implying that in this context strong ties are key. Qualitative research suggests the nature of people’s networks and their positions within them play an important role in the command over labour: “the famous always get their work done

Using social networks to understand and overcome implementation barriers in the global HIV response

Background: Despite the development of several efficacious HIV prevention and treatment methods in the past 2 decades, HIV continues to spread globally. Uptake of interventions is nonrandomly distributed across populations. Such inequality is socially patterned and reinforced by homophily arising from both social selection (becoming friends with similar people) and influence (becoming similar to friends). Methods: We conducted a narrative review to describe how social network analysis methods—including egocentric, sociocentric, and respondent-driven sampling designs—provide tools to measure key populations, to understand how epidemics spread, and to evaluate intervention take-up. Results: Social network analysis–informed designs can improve intervention effectiveness by reaching otherwise inaccessible populations. They can also improve intervention efficiency by maximizing spillovers, through social ties, to at-risk but susceptible individuals. Social network analysis–informed designs thus have the potential to be both more effective and less unequal in their effects, compared with social network analysis–naïve approaches. Although social network analysis-informed designs are often resource-intensive, we believe they provide unique insights that can help reach those most in need of HIV prevention and treatment interventions. Conclusion: Increased collection of social network data during both research and implementation work would provide important information to improve the roll-out of existing studies in the present and to inform the design of more data-efficient, social network analysis–informed interventions in the future. Doing so will improve the reach of interventions, especially to key populations, and to maximize intervention impact once delivered

Algorithmic and Statistical Perspectives on Large-Scale Data Analysis

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.Comment: 33 pages. To appear in Uwe Naumann and Olaf Schenk, editors, "Combinatorial Scientific Computing," Chapman and Hall/CRC Press, 201