Jürgen Lerner

Longitudinal analysis of personal networks : the case of Argentinean migrants in Spain

This paper discusses and illustrates various approaches for the longitudinal analysis of personal networks (multilevel analysis, regression analysis, and SIENA). We combined the different types of analyses in a study of the changing personal networks of immigrants. Data were obtained from 25 Argentineans in Spain, who were interviewed twice in a 2-year interval. Qualitative interviews were used to estimate the amount of measurement error and to isolate important predictors. Quantitative analyses showed that the persistence of ties was explained by tie strength, network density, and alters’ country of origin and residence. Furthermore, transitivity appeared to be an important tendency, both for acquiring new contacts and for the relationships among alters. At the network level, immigrants’ networks were remarkably stable in composition and structure despite the high turnover. Clustered graphs have been used to illustrate the results. The results are discussed in light of adaptation to the host society.

Network analysis of collaboration structure in Wikipedia

In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles.

Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data

With few exceptions, statistical analysis of social networks is currently focused on cross-sectional or panel data. On the other hand, automated collection of network-data often produces event data, i.e., data encoding the exact time of interaction between social actors. In this paper we propose models and methods to analyze such networks of dyadic events and to determine the factors that influence the frequency and quality of interaction. We apply our methods to empirical datasets about political conflicts and test several hypotheses concerning reciprocity and structural balance theory.

Algorithmics of Large and Complex Networks

Network Algorithms.- Design and Engineering of External Memory Traversal Algorithms for General Graphs.- Minimum Cycle Bases and Their Applications.- A Survey on Approximation Algorithms for Scheduling with Machine Unavailability.- Iterative Compression for Exactly Solving NP-Hard Minimization Problems.- Approaches to the Steiner Problem in Networks.- A Survey on Multiple Objective Minimum Spanning Tree Problems.- Traffic Networks.- Engineering Route Planning Algorithms.- From State-of-the-Art Static Fleet Assignment to Flexible Stochastic Planning of the Future.- Traffic Networks and Flows over Time.- Communication Networks.- Interactive Communication, Diagnosis and Error Control in Networks.- Resource Management in Large Networks.- Multicast Routing and Design of Sparse Connectors.- Management of Variable Data Streams in Networks.- Models of Non-atomic Congestion Games - From Unicast to Multicast Routing.- New Data Structures for IP Lookup and Conflict Detection.- Network Analysis and Simulation.- Group-Level Analysis and Visualization of Social Networks.- Modeling and Designing Real-World Networks.- Algorithms and Simulation Methods for Topology-Aware Sensor Networks.

Visone Software for Visual Social Network Analysis

We are developing a social network tool that is powerful, comprehensive, and yet easy to use. The unique feature of our tool is the integration of network analysis and visualization. In a long-term interdisciplinary research collaboration, members of our group had implemented several prototypes to explore and demonstrate the feasibility of novel methods. These prototypes have been revised and combined into a stand-alone tool which will be extended regularly.

Visual Statistics for Collections of Clustered Graphs

We propose a method to visually summarize collections of networks on which a clustering of the vertices is given. Our method allows for efficient comparison of individual networks, as well as for visualizing the average composition and structure of a set of networks. As a concrete application we analyze a set of several hundred personal networks of migrants. On the individual level the network images provide visual hints for assessing the mode of acculturation of the respondent. On the population level they show how cultural integration varies with specific characteristics of the migrants such as country of origin, years of residence, or skin color.

Summarizing Dynamic Bipolar Conflict Structures

We present a method for visual summary of bilateral conflict structures embodied in event data. Such data consists of actors linked by time-stamped events and may be extracted from various sources such as news reports and dossiers. When analyzing political events, it is of particular importance to be able to recognize conflicts and actors involved in them. By projecting actors into a conflict space, we are able to highlight the main opponents in a series of tens of thousands of events and provide a graphic overview of the conflict structure. Moreover, our method allows for smooth animation of the dynamics of a conflict

Structural similarity in graphs

Standard methods for role assignment partition the vertex set of a graph in such a way that vertices in the same class can be considered to have equivalent roles in the graph Several classes of equivalence relations such as regular equivalence and equitable partitions have been proposed for role assignment, but they all suffer from the strictness of classifying vertices into being either equivalent or not It is an open problem how to allow for varying degrees of similarity Proposals include ad-hoc algorithmic approaches and optimization approaches which are computationally hard. In this paper we introduce the concept of structural similarity by relaxation of equitable partitions, thus providing a theoretical foundation for similarity measures which enjoys desirable properties with respect to existence, structure, and tractability.

Structural Similarity: Spectral Methods for Relaxed Blockmodeling

In this paper we propose the concept of structural similarity as a relaxation of blockmodeling in social network analysis. Most previous approaches attempt to relax the constraints on partitions, for instance, that of being a structural or regular equivalence to being approximately structural or regular, respectively. In contrast, our approach is to relax the partitions themselves: structural similarities yield similarity values instead of equivalence or non-equivalence of actors, while strictly obeying the requirement made for exact regular equivalences. Structural similarities are based on a vector space interpretation and yield efficient spectral methods that, in a more restrictive manner, have been successfully applied to difficult combinatorial problems such as graph coloring. While traditional blockmodeling approaches have to rely on local search heuristics, our framework yields algorithms that are provably optimal for specific data-generation models. Furthermore, the stability of structural similarities can be well characterized making them suitable for the analysis of noisy or dynamically changing network data.

Structural balance in signed networks: Separating the probability to interact from the tendency to fight

Abstract Structural balance theory implies hypothetical network effects such as “the enemy of an enemy is a friend” or “the friend of an enemy is an enemy.” To statistically test such hypotheses researchers often estimate whether, for instance, actors have an increased probability to collaborate with the enemies of their enemies and/or a decreased probability to fight the enemies of their enemies. Empirically it turns out that the support for balance theory from these tests is mixed at best. We argue that such results are not necessarily a contradiction to balance theory but that they could also be explained by other network effects that influence the probability to interact at all. We propose new and better interpretable models to assess structural balance in signed networks and illustrate their usefulness with networks of international alliances and conflicts. With the new operationalization the support for balance theory in international relations networks is much stronger than suggested by previous work.