# BENet 2014 Abstracts

#### Keynote

Mark Tranmer (CMIST, School of Social Sciences, University of Manchester)
Multilevel Networks, and the Multilevel Analysis of Networks
Abstract:

Multilevel networks can be defined, for two levels, as a set of lower level (level 1) nodes and their connections, a set of higher level (level 2) nodes, and their connections, and a perhaps the cross-level network between the level 1 nodes and the level 2 nodes. An example would be academics (level 1 nodes) who collaborate within and between universities on their research (the level 1 network). Universities (level 2 nodes) that set-up student exchange programmes, or shared degree programmes (the level 2 network), and perhaps individual academics (level 1 nodes) that are invited to be external examiners at universities (level 2 nodes) other than their usual place of work. Given such a multilevel network, our target of inference might be understanding the nature of this multilevel structure at a given point in time, or might be understanding how an attribute of the level 1 nodes varies between the level 1, level 2, and cross level networks in an analysis of multilevel network dependencies.

Multilevel analysis is sometimes useful for testing substantive hypotheses relating to social networks. However, the multilevel analysis of a network does not automatically make that network a multilevel network. For example, multilevel analysis is very useful for the analysis of single-level ego-nets, and has an important role to play in multiplex network dependencies for a single level of network nodes. However, given certain targets of inference, a multilevel analysis can be also very useful for analysing a multilevel network.

These subtle differences can be confusing. I this talk I seek to clarify the difference between multilevel networks and the multilevel analysis of networks, and to highlight these differences with real-data examples of the multilevel analysis of single-level and multilevel networks. I will also give an example of when a multilevel analysis is not the right approach for a multilevel network.

#### Session I

Chair: Glenn Magerman (KUL)

Samuel Standaert, Benjamin Vandermarliere and Stijn Ronsse (UGent). Mapping historical integration

Gautier Krings, Jean-François Carpantier and Jean-Charles Delvenne (UCL). Trade Integration and Trade Imbalances in the European Union: A Network Perspective

Sophie Béreau, Jean-Yves Gnabo and Donald Zountcheme (UCL/UNamur). Risk sharing vs. risk contagion in financial systems: An empirical investigation
Abstract: The recent financial crisis has fostered a renewed interest in systemic risk. If numerous measures have been developped in the literature to cope with the various dimensions of the phenomenon, a recent literature has emphasized the need for more explorations through the lens of complexity science. More specifically, representing financial institutions and their relationships as the vertices and edges of an oriented graph capturing the underlying structure of the financial market as a complex adpative system can help to better understand the nature of mechanisms at stake during contagion episodes. In this paper, we propose an empirical strategy to assess on real data, the impact of the financial network structure on systemic risk and more specifically, to test the existence of a tipping point as conjectured by Haldane (2009) and further modelled in the recent contribution of Acemoglu et al. (2013). Our results suggest that in addition to the traditional firm-level determinants, network structure significantly impacts systemic risk as more central nodes appear to represent the more systematically important financial institutions of our sample. In addition, we do detect threshold effects in the impact of large adverse shocks when combining with high level of closeness centrality, which tends to confirm Acemoglu et al's theoretical predictions.

Dynamic interbank trust networks
Abstract: In this paper, we propose an agent-based model (ABM) of the interbank market in which agents' (banks) bilateral lending/borrowing decisions are driven primarily by the level of trust between every pair of banks in the system. This approach provides a behavioural foundation for the freeze in interbank credit that occurred in the wake of the collapse of Lehman Brothers in 2008. Moreover, we embed the ABM in a network model calibrated to replicate real observed properties of interbank markets (namely, scale-free behaviour and disassociative mixing). Consequently, each bank represents a node in the network with the (weighted, directed) edges representing the bilateral exposures (i.e. the lending and borrowing relationships) between them. This network is taken as an initial condition for our dynamic approach, whereby edge weights are endogenously updated as banks suffer exogenous shocks and provide/request liquidity on the interbank market. We begin by assuming that each agent is characterized by a bilateral lender trust, which characterizes how much he trusts each of his borrowing counterparties and a borrower trust characterizing i's level of trust for his neighbours from whom he borrows. Note that the number of lending/borrowing neighbours for each i is determined by the in/out degree, respectively of the node. The initial lender (borrower) trust parameter ascribed to each node is simply given by the inverse of the in (out) degree (this is intuitive as it implies that banks with fewer connections have a stronger trust relationship with each of their counterparties. Indeed, banks have been observed to engage in relationship lending with each other.) Dynamic trust between banks enters via simple heuristics on how to redistribute total lending and borrowing amongst potential borrowers and lenders. Specifically, we assume that each bank first determines potential lending and borrowing as a function of their lender and borrower trust parameters. Effective lending and borrowing is obtained by matching borrowing requests with loan provision for each pair ij and taking the minimum of the two. In the first of two endogenous trust updates, borrowing banks update their trust in each lending counterparty as a function of the difference between how much they requested and how much they received. From a network perspective, this varies the weight of the edge in question.

#### Session II

Chair: Dirk Jacobs (ULB)

Jef Vlegels and John Lievens (UGent). Music classification, genres, and taste patterns: a ground-up network analysis on the clustering of artist preferences
Abstract: This article reflects on the use of predetermined genre lists to measure patterns in music taste and, more specifically, cultural omnivorousness. The use of a predetermined array of genres assumes that music genres are rigid and stable concepts, whereas in reality genre boundaries continually emerge, evolve, and disappear. Inspired by Lamont’s (2010) call to study classification systems ‘from the ground up’, we present an alternative strategy to measure patterns of music taste using an open question about artist preferences. We build a two-mode network of artists and respondents to identify clusters of respondents that have similar relationships to the same set of artists. Our results show that research using measurements of cultural omnivorousness based on genre preferences might be hampered, as it misses important subdivisions within genres and is not able to capture respondents who combine specific aspects within and across music genres.

Chloé Meredith, Charlotte Struyve and Sarah Gielen (KUL). 'Fitting in’: Does it make a difference for teachers’ job satisfaction?
Abstract: For many years, educational research has focused on the job satisfaction of teachers to explain well-being, absenteeism, school quality and the decision to stay or leave the profession (Ingersoll & Smith, 2003). Job satisfaction is therefore one of the most frequently investigated job attitudes and can be defined as “the pleasurable or positive emotional state resulting from the appraisal of one’s job and job experience” (Locke, 1976, p.1300). Recent decades, researchers in organization studies increasingly used the social integration or ‘fit’ in the organization to explain job satisfaction from a contextual perspective. Based on the literature, this fit in an organization can be conceptualized in several ways. One way is person-organization fit (P-O fit), which reflects the compatibility between a person and the organization (Kristof, 1996). P-O fit can be measured on various dimensions, but this study uses value congruence, one of the most commonly used measures. Multiple studies already provided evidence that the value congruence of an employee is linked to his or her job satisfaction (Bretz Jr & Judge, 1994; Silverthorne, 2004). A second way to conceptualize the fit of an individual is the social-structural position in the organizational network. Studies using embeddedness theory as a framework indicate that the ‘links’ a person has are crucial for the social integration in the organization (Granovetter, 1985). ‘Links’ can then be described as the amount of ties individuals have with other people and activities at work (Mitchell, Holtom, Lee, Sablynski, & Erez, 2001). Thomas and Feldman (2007) indicated that the more links a person has, the more professionally and personally tied this person will feel to the organization. Often, researchers investigate which network configurations lead to a higher job satisfaction (Lee & Kim, 2011). Previous research in non-educational settings proved that the centrality of an actor has positive effects on the job satisfaction (Kilduff & Krackhardt, 1994) and the employee retention (Mossholder, Settoon, & Henagan, 2005). In educational research, there has been a limited amount of attention for the fit of teachers in their school, especially focusing on the integration or isolation of teachers. Bakkenes, De Brabander & Imants (1999) proved that teacher isolation causes absenteeism and low job satisfaction. Skaalvik and Skaalvik (2011) found that the extent to which teachers share values with other school team members in their school is important for their job satisfaction. Others have argued that collegial relationships and integration are important predictors for the satisfaction teachers perceive from doing their job (Xin & MacMillan, 1999). However, limited attention has been paid to the PO-fit and social-structural fit of teachers. Given that organizational attitudes and behaviours are socially constructed, this study aims to provide clarity on whether and to what extent the fit of teachers can be associated with their job satisfaction. To provide an answer, both attribute and social network data of approximately 1050 school team members, working in 14 secondary schools in Flanders were gathered. The attribute data that were collected concerned several attitudes about the profession and the school as a workplace, such as job satisfaction and the desired and actual collaboration in the school. Based on the latter two, different measures of value congruence can be calculated: perceived P-O-fit and objective P-O fit (O'Reilly, Chatman, & Caldwell, 1991). Perceived P-O fit is constructed by calculating the difference between the individual’s desired collaboration and how the individual perceives the actual collaboration in the school, while objective P-O fit compares the individual’s perception on the desired collaboration with the perception of the school team on the actual collaboration. Although both operationalize the concept of P-O fit, previous research found disparate findings due to the difference in measurement (Verquer, Beehr, & Wagner, 2003). As this study is one of the first to explore the P-O fit of teachers, both measures will be included in the analyses. Relational data were derived from two sociometric questions concerning the information and the social support network. Social network analysis is the most appropriate method to analyse relational data whereby relations are seen as the linkages between agents (Scott, 2004). The basic building block of social networks is the tie and the presence or absence of it, which makes it possible to calculate several network measures, such as indegree centrality, betweenness centrality and closeness centrality (Borgatti & Foster, 2003). UCINET (Borgatti, Everett, & Freeman, 2002) will be used to compute the network centrality measures. To investigate if and to what extent the several measures of fit can be related to teachers’ job satisfaction, regression models will be conducted.

Nina Eggert and Katia Pilati (UAntwerpen/Univ. Trento). Organizational networks in the field of immigration
Abstract: The objective of this paper is to analyze the formation and structure of organizational networks in the field of immigration in a comparative perspective. More specifically, we will test hypotheses of the impact of specific opportunities in the field of immigration on migrants’ organizational networks by analyzing collaborations of migrant organizations with other migrant and native organizations and the prevailing logics of interaction. Our main argument is that the political context of migrants’ city of residence affects the way migrant organizations send ties to other migrant and non-migrant organizations active in the field. To test our hypotheses we will use a unique data set of an organizational survey of migrant organizations in five European cities: Budapest, Lyon, Madrid, Milan and Zurich and analyze the networks of the total population of migrant organizations in each city.

The structure of ethnic social capital: two-mode analysis of interlocks among immigrant organizations
Abstract: The presence of a well connected civic elite, linking the different organizations expressed by an ethnic community, is considered an important determinant of the political participation and trust of minority groups [Fennema & Tillie, 2008]. Previous quantitative work on this topic has been limited to simple structural measures on one-mode projections on the organization mode [Fennema & Tillie, 1999, 2001, 2008; Vermeulen & Berger, 2008]. We remark that the projection introduces biases in some of the measures, increasing the number of ties in a combinatorial fashion, and propose instead a structural analysis of the unprocessed two-mode networks. Inspired by existing measures of hierarchy in one-mode networks [Everett & Krackhardt, 2012], we consider different measures of clustering and redundancy that have been developed for two-mode data [Latapy et al, 2008; Opsahl, 2013]: additionally, we look for correlations among these measures and node degrees, as in [Latapy et al, 2008]. Using data from [Vermeulen, Berger, 2008], on Amsterdam and Berlin, and additional data on Brussels, we compare the association networks developed by the Turkish and Moroccan communities in different host countries, characterized by different political opportunity structures. We further discuss how the proposed measures can be used to identify the different structures described in previous work based on one-mode projections, such as umbrella organizations and cliques.

#### Session III

Chair: Matteo Gagliolo (ULB)

Manuel Förster (USL). Strategic Communication in Social Networks
Abstract: We study the role of conflicting interests in long-run belief dynamics. Agents meet pairwise with their neighbors in the social network and exchange information strategically. We disentangle the terms belief (what is held to be) and opinion (what is ought to be due to a bias): the sender of information would like to spread his opinion (biased belief), while the receiver would like to learn the sender's true belief. In equilibrium the sender only communicates a noisy message containing information about his belief. The receiver interprets the sent message and updates her belief by taking the average of the interpretation and her pre-meeting belief. With conflicting interests, the belief dynamics typically fails to converge: each agent's belief converges to some interval and keeps fluctuating on it forever. These intervals are mutually confirming: they are the convex combinations of the interpretations used when communicating, given all agents hold beliefs in the corresponding intervals.

Luis Rocha (UNamur/Karolinska Inst.). Epidemics and diffusion in temporal networks
Abstract: Networks have been used to model systems as different as power-grids, flights between airports, or social relations. The standard approach is to collect all interactions between the elements, or nodes, forming the system during a certain period and study the static structure of connections between them. Diverse network structures are typically associated to different constrains that supposedly regulate dynamic processes taking place on the networks, as for example, epidemics or communication. In some contexts, however, these interactions are also dynamic and thus the network structure varies in time. If the dynamics on and of the network occur at the same scale, the temporal patterns of node and link activity may affect non-trivially the diffusion dynamics. In this talk, I aim to briefly introduce the concept of temporal networks and review some of my research related to this topic. I will present an original large dataset of sexual contacts between sex-workers and their clients and some results on the spread of simulated infections in this network, discussing the impact of temporal (particularly bursts of vertex activity) and static (particularly network clustering) structures in the prevalence of infections. If time permits, I will briefly present some results about random walk dynamics in temporal networks. Some emphasis will be given to present an original random walk centrality measure for temporal networks, based on the adjacency matrix at each time step, and to recent results where we propose that temporal heterogeneities may hinder the importance of network structure to regulate the diffusion on networks, particularly, to regulate the convergence time of a stochastic process to the equilibrium. Considering the nature of the workshop, more emphasis will be given to results and insights than to details on methods and calculations.

Jean-Jacques Herings, Ana Mauleon and Vincent Vannetelbosch (UMaastricht/USL/UCL). Stability of Networks under Limited Farsightedness

Martin Gueuning, Renaud Lambiotte and Jean-Charles Delvenne (UNamur/UCL). Burstiness, efficiency and transmissibility of infectious contacts in temporal networks
Abstract: 1 Introduction In this work, we are looking at an epidemic-like propagation process on a network of agents. We suppose that the time between two consecutive meetings is random and described by a given inter-meeting time probability distribution, which is typically a power-law for human-related networks ([1]), and that at each meeting an infected individual transmits the disease to his/her neighbours with some probability p. The goal is to determine the effective inter-event time distribution of the underlying process, that is the distribution of the time it takes for an individual to transmit the disease to his/her neighbour once he/she is infected. Knowing this allows us to compare different behaviour showing same characteristics such as the mean time between two successful transmissions but driven by different inter-meeting time distribution. 2 Results Working in the Laplace domain and with probability generating function, one can analytically determine the first and second moments of the inter-event time distribution, that directly give the mean and variance of this process, in function of p and the moments of the inter-meeting time distribution. From these results, we obtain some important values of this diffusion process such as the average relay time, which is a standard measure of the burstiness of a process ([2]). Once recovery is taken into account, we can find the transmissibility P of the diffusion, which is the overall probability that individual transmits the infection before recovery. In the case of a tree-network, this transmissibility directly gives the reproduction number R0, and therefore the epidemic threshold. We also find that for a given average time <t>/p between two infectious contacts, rarer (high <t>) but more ’efficient’ contacts (high p) lead to less bursty (low relay time) but more transmissible contacts, that is higher probability of transmitting the disease before recovery once infected. 3 Conclusion In this work, we could determine important values such a average relay time or transmissibility of an epidemic-like propagation process taking place on a network of agents. We find that the mean time between two successful transmissions is not sufficient to characterize the diffusion speed of the epidemics, as between two agents with the same mean time of successful transmission, the one with fewer but more efficient contacts has a higher transmissibility for the disease, that is a higher probability of transmitting the disease to his/her neighbours before recovery. References [1] A. L. Barabasi (2005). ”The origin of bursts and heavy tails in human dynamics”, Nature, 435(7039), 207-211. [2] M. Kivela et al. (2012). ”Multiscale analysis of spreading in a large communication network,” Journal of Statistical Mechanics: Theory and Experiment, 2012(03), P0300 [3] R. Lambiotte et al., “Burstiness and spreading on temporal networks,” European Physical Journal B. Condensed Matter and Complex Systems, (2013).

#### Poster session

Claire Lagesse (Univ. Paris 7). A "Way" to explore space networks
Abstract: Space networks have a great capacity to capitalize information through space and time. To demonstrate this hypothesis we focus on the road network, extracting from cities the skeleton of their streets. The point is to understand how by extracting information as simple as the elementary geometry and topology of those networks, one could describe a very complex object such as a city. Road network continuity has been deeply studied, leaning on social perception or creating a dual to explore its centralities. But, most of the time, the particular geographical property of such a network has been forgotten. To ensure the relevance of our study, we create a complex object, named way. It is fundamentally anchored in space as it is a geographical object constructed with local rules associating arcs at each crossing. As we want to create a generic multi-scale element which could be meaningful as well for physicists as for town planers, we developed three methods of construction. The first (Method 0) favor the straight line (local alignment between two arcs at a crossing) ; the second (Method 1) favor a global alignment at the intersection ; the third (Method 2) associates randomly the arcs at each crossing to ensure the significance of such a reconstruction. The continuity is determined through a limit of deviation angle (threshold angle). By being independent of the toponymy, the way allows to analyze road structures independently of administrative borders. Considering also only the network skeleton, it does not depends on road use and treatment, which are subjected to changes in time. The whole space is considered. Circulation is not hindered by something else than geometry. Several geometrical and topological characteristics have been studied on ways. For instance, the angles distribution was considered, for the originals arcs (which reveal a clear attraction for alignment or perpendicularity as we observe peaks for 0° and 90° angles) and for the ways. Another interesting road network property is how the logarithm of ways length fit a Gaussian curve. With those first observations, one could appreciate the concordance of results on very different spaces. Once the method and the threshold angle scientifically chosen, the study was pursued by developing more sophisticated indicators, taking into account the whole network and the relation of a specific way with it. Two indicators of this kind will be explained here. First one is the connectivity (number of segments from other ways connected to a particular one), which appear to be relevant as it highlights some historical main axis, as old access roads of Paris. Keeping in mind the importance of the alignment correlated to the cost of turning, we will present a second indicator, the structurality (measuring the centrality of a way considering way connections to the whole network). This indicator has for first characteristics to be very stable in space. To visualize the impact of the sample borders, several sub part of the large Avignon (city of the South of France) have been considered. The stability of the indicator in space is shown by observing that the most structural ways remain the same whatever the sample borders are. Even if main coherent axis are cut, the coherence of the network remain stable, and only the very truncated ways are affected. This is in high contrast to the case where one consider only the topological distance on the original graph constituted of arcs and not ways. In this case, the most important arcs changes drastically depending on the sample chosen, and it does not reveal historical roads but either speedways (when their continuity is respected) or the center of the map. The second characteristics of the structurality is to reveal the history of the network. For a sample chosen to cover Avignon and its surrounding, the outer walls and historical access roads are highlighted. For Paris, the historical center appear to be the most structural for the city. In general, we see that with only a local rule, computed at each vertex, one can elaborate more complex and multi-scale elements to analyze the deep structure of a reticular network. The use of the alignment as criteria to construct an hypergraph of ways reveal to be very powerful, as allowing to recover both the structure of a city and its history. The geometry of the spatial networks associated to the topological information is thus relevant to analyze them.

Vsevolod Salnikov and Renaud Lambiotte (UNamur). Community detection in temporal networks
Abstract: The field of community detection has attracted much attention in recent years. If efficient methods exit for overlapping or non-overlapping communities in static networks, the problem of finding communities in temporal networks is still a challenge. Basic approaches consider the temporal system as a sequence of static networks where standard methods can be used. Alternative approaches represent the system by a tensor to be clustered. The main purpose of this work is to develop a statistical approach taking advantage of the temporal correlations between edges in order to uncover overlapping, synchronized communities in networks.

Tarik Roukny and Daniel Fricke (ULB). Competititive and Mutualistic Relationships in Financial Networks
Abstract: Many interaction matrices in natural communities have been found to be share certain properties, with nestedness being of particular interest in recent years. In this paper, we provide evidence that networks arising from credit relationships between banks and firms display a significantly nested structure. These networks combine mutualistic (bank-sector relationships) and antagonistic tendencies (competition between bank and sectors, respectively). Furthermore, we introduce a dynamical model of such system in order to shed light on important policy issues in financial ecosystems. In particular, we explore the circumstances under which nested architectures arise, and investigate the impact of competition and mutualism on both system stability and biodiversity.

Sarah de Nigris and Xavier Leoncini (Centre de Physique Théorique, Marseille). Crafting networks to achieve (or not) chaotic states.
Abstract: When dealing with systems of interacting agents, beyond the understanding of their deep principles, naturally arises the question of prediction and control of their possible collective behaviors. Indeed it appears of fundamental importance, for instance in the artificial system design, to ask oneself if the system is going to display a collective behavior or, for instance, its response will be chaotic. In this work we study the influence of network topology on collective properties of a dynamical system defined upon it. For this purpose, we propose a network model in which links of a regular chain are rewired according to a probability $p$ within a specific range $r$. We then focus on how the thermodynamic behavior of a dynamical system, the $XY-$rotors model, is affected by the controlled topological changes of $(p,r)$. We identify a quantity, the network dimension $d(p,r)$ as a crucial parameter. Varying this dimension we are able to cross over from topologies with $d<2$ exhibiting no phase transitions to ones with $d>2$ displaying a second order phase transition, passing by topologies with dimension $d=2$ which exhibit states characterized by infinite susceptibility and macroscopic chaotic/turbulent dynamical behavior. More in detail, this work inscribes itself in the frame of control issues: we provide here a mean to construct a class of networks which, through to controlled topological changes, can give rise to a whole range of dynamical and statistical behaviors among which a chaotic state of infinite susceptibility. We then related the different dynamical behaviors to the network dimension. In our network model, we put essentially three ingredients: first we impose the condition of sparseness, second we introduced the concept of interaction range constraining the links to be at most of a fixed length $r$ and last we inject randomness in the structure so to have a non uniform degree. Practically we proceed to a construction similar to the Watts-Strogatz one for Small World networks: we rewire each link with probability $p$ but we impose to rewire it within a range $r$. Tuning $p,r$ the network dimension due to the presence of long-range links. We then consider, as dynamical system, $N$ $XY-$rotors, whose dynamics is described by an angle $\theta_(t)$ and the momentum $p_(t)$. Each rotor $i$ is located on a network vertex and its interactions are provided by the set of vertices attached to it via the links. We thus run molecular dynamics (MD) simulations of the isolated system and, in order to grasp the amount of coherence in the system, we chose the average magnetization $M=\left|\mathrm}\right|$ as order parameter $\mathrm}=\frac1\sum_\left(\cos\theta_,\sin\theta_\right)$. Finally, our results for $r\sim\sqrt$ and $r\sim N^$ point to a strong correlation between the different thermodynamic behaviors and the dimension of the underlying network.

Kristel Vignery (USL). Integrating online social networks in student achievement prediction techniques