Posters and Demonstrations
People are subject to social influence as well as mass media influence. Individuals are therefore confronted to different sources of information which can have an impact on the topics they discuss. In this poster, such interactions are modelled by networks with different types of entities and links which are well adapted to reflect the different meanings of interactions. For instance, the question is whether the “individuals-topics” interactions in social media are random events or whether they are related to other existing interactions between the nodes. We propose a path-constrained random walk to perform statistical regression on networks and to thus unveil the full network topology.
We show non-trivial dynamical effects of cooperation and competition in an ecological framework [1-5]; we study two (or more) spreading dynamics competing with each other for host resources but they can choose cooperation as successful strategy too. We treat dynamics in a homogeneously mixed population by means of mean-field theory and stability analysis and also on complex transmission networks. We study the impact of cooperation on the outcome of the global dynamics, which can be quantified in terms of dominance of one competing agent or the co-circulation of both of them. We show that the presence of cooperation can alter the outcome of competition as it may favor the more cooperative dynamics over the more infectious one.
 L. Chen, F Ghanbarnejad, W. Cai, P. Grassberger, EPL 104, 5 (2013).
 W. Cai, L. Chen, F. Ghanbarnejad, P. Grassberger, Nature Physics 11, 936 (2015).
 P. Grassberger, L. Chen, F. Ghanbarnejad, W. Cai, Physical Review E 93, 042316 (2016).
 J. P. Rodríguez, F. Ghanbarnejad, V. M. Eguíluz, Frontiers in Physics, V 5, P 46 (2017).
 L. Chen, F. Ghanbarnejad, D. Brockmann, New J. Phys. 19, 103041(2017).
Depending on paremeterization, basic Bounded Confidence models lead either to complete consensus or complete fragmentation of the opinion space. Noise terms can be introduced to achieve more realistic opinion dynamics. While this produces intuitively plausible results, it isn't easily mapped to current theories of social action and discourse. The detailed dual process models from Social Psychological Persuasion and Social Influence Studies in contrast use momentary energy and discourse investment to explain why an individual would evaluate messages systematically based on content in one situation and heuristically via meta-attributes (e.g. sender attributes) in another. This project integrates Social Psychological dual process models of information processing with the Model of Sociological Explanation from Sociology. The latter is a dual process model of social action, with the two modes corresponding to "slow", rational planning of actions and "fast", automatic selection of scripts based on familiarity with a situation. The situation classification uncertainty of inputs as well as momentary energy is taken into account to model the selection of one of the two modes in the sense of a latent utility function. This allows for a very general notion of norm generation. A layered dual process model of discourse decisions is constructed: In the inner layer, individuals are interested in maximizing opinion similarity in their neighborhood and minimizing energy loss, while also maximizing self-cohesion and self-neighborhood-cohesion. Decisions are made regarding the sending of "optimized" or "unoptimized" opinions as messages to maximize adoption probability as well as the reception mode (systematic or heuristc). The outer layer consists of the Model of Sociological Explanation: Individuals try to classify the discourse situation based on the available inputs and, if the uncertainty is too high and/or their energy is low, will choose to make all decisions in the rational mode as opposed to the automatic selection of situation-associated scripts concerning the inner layer. The project aims to implement this full model for the Hegselmann-Krause model in a step-wise fashion to ascertain minimal conditions for lasting, yet systematic dynamics. The goals for this project are then threefold: a) Determine the aggregation dynamics of dual process model assumptions from Persuasion Studies which were previously studied for small-groups, b) Link Opinion Dynamics models more closely with contemporary theory, while keeping complexity minimal and c) shift the focus of analysis toward actions, motivations, their local clustering and thus toward a more sociological research question. The account up to the inner layer is implemented for the Hegselmann-Krause model and shows plausible dynamics in exploratory runs. A first sensitivity analysis will be conducted and results will be presented on the poster.
The network of alliances and oppositions determine to a large extend the landscape of the political system. The instability of the system crucially hinges on the ties amongst the political actors and their temporal readjustment. Therefore these systems are amenable to a description by adaptive co-evolving networks, such as the autocatalytic network model by Jain and Krishna. In that model a network of species with catalytic ties is evolving via the deletion species that do not profit from the network and via the introduction of new species tying into the existing network. It exhibits the creation of stable autocatalytic subsystems as well as the growth of latent subsystems that can lead to partial or full collapse of catalytic subsystems. We propose an interpretation of this model in terms of political actors and their alliances and suggest that political systems may suffer similar collapse mechanisms during coup d'etats or revolutions. We investigate collapses and early-warning signs in this model and show how impending collapses can be detected.
This demonstration shows the current state of our Outlier Explorer, a Web service dedicated to the study of multidimensional datasets and for the detection of statistical outliers within. It is mainly a tool for data exploration allowing to have a first glance at it and to formulate research hypotheses to be later tested. This component takes as input a list of numeric observations described according to several categorical dimensions. For example, in the case of Twitter data, it can be the number of tweets (numeric observation) that have been published by a given user (first dimension) about a given topic (second dimension) at a given date (third dimension). Statistical outliers are then identified by first selecting some dimensions of interest, that is by subsetting or by aggregating the input dimensions. If needed, observations can also be normalised according to the marginal values along the selected dimensions, thus comparing the observed value to an expected value obtained by the uniform redistribution of the selected marginal values. Different statistical tests can then be chosen to measure the deviation between the observed and the expected values. The component finally returns a list of positive outliers, that is observations that are significantly higher than expected. This Web interface is hosted by the Huma-Num facility for digital humanities. Sources are open and available on GitHub.
In this demonstration, we introduce a module for the Penelope API by the mean of a few simple examples. The module is designed to compress multidimensional graph-like data. The compression of data is, in our case, the construction of clusters of similar datapoints within a rectangular subspace of the dataset. We will use two datasets to explain these ideas and what can be expected from the data compression framework: the “Karate club” classical toy-graph and a corpus of geo-media data containing newspaper articles about international events. The demonstration will go from the interpretation of a single cluster to the multiscale compression of the datasets. We can also provide quick technical insights about how the algorithm works and is implemented.
Twitter is now an integral part of means of communication used by political leaders to disseminate information to the public. A politician may use it sporadically to merely broadcast to his followers or on the contrary employ it regularly and tweet at strategic moments. Likewise, their followers may be occasional spreaders or more involved online activists primarily retweeting a particular political figure. In this poster, we propose a multidimensional analysis method to describe structural and temporal relational patterns in a retweet network: the first step is to identify which type of irregular pattern one wants to detect, the second is to measure its activity, finally, the third step is to compare this activity to an expected activity so as to determine whether it is abnormal or not. We show that varying the scale of observation in each of these steps leads to interesting results such as the highlighting of political leaders or media events.