Nino Antulov-Fantulin

Cohesiveness in Financial News and its Relation to Market Volatility

Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets.

Identification of Patient Zero in Static and Temporal Networks : Robustness and Limitations

The detection of an epidemic source or the patient zero is an important practical problem that can help in developing the epidemic control strategies. In this paper, we study the statistical inference problem of detecting the source of epidemics from a snapshot of a contagion spreading process at some time on an arbitrary network structure. By using exact analytic calculations and Monte Carlo simulations, we demonstrate the detectability limits for the SIR model, which primarily depend on the spreading process characteristics. We introduce an efficient Bayesian Monte Carlo source probability estimator and compare its performance against state-of-the-art approaches. Finally, we demonstrate the applicability of the approach in a realistic setting of an epidemic spreading over an empirical temporal network of sexual interactions. Introduction The majority of biological, technological, social and information systems structures can be represented as a complex network [1, 2, 3]. The most prevalent type of dynamic processes of public interest characteristic for the real-life complex networks are contagion processes [4]. Different mathematical methods have been used to study the epidemic spreading on complex networks including the bond percolation method [7, 8], the mean-field approach [9, 10], the reaction-diffusion processes [11, 12], pair and the master equation approximations [13] as well as models with the complex compartmental structure along with population mobility dynamics [14]. Epidemiologists detect the epidemic source or the patient-zero either by analysing the temporal genetic evolution of virus strains [5] or try to do a contact backtracking [6] from the available observed data. However, in cases where the information on the times of contact is unknown, or incomplete, the backtracking method is no longer adequate. This 1 ar X iv :1 40 6. 29 09 v1 [ cs .S I] 1 1 Ju n 20 14 becomes especially hard if the only data available is a static snapshot of a epidemic process at some time. Even in cases when we do have some information on the times of contact, the longer the recovery time and subtler the symptoms the harder it becomes to establish the proper ordering of the transmissions that have occurred. Due to its practical aspects and theoretical importance, the epidemic source detection problem on contact networks has recently gained a lot of attention in complex network science community. This has led to the development of many different source detection estimators for static networks, which vary in their assumptions on the network structure and the spreading process models [15, 16, 17, 18, 19, 20, 21, 22, 23]. For the source detection with the SI model the following interesting results have been obtained. Zaman et. al. developed a rumor centrality measure, which is the maximum likelihood estimator for regular trees under the SI model [15]. Dong et. al. also studied the problem of rooting the rumor source with the SI model and demonstrated the asymptotic source detection probability on regular tree-type networks [16]. Comin et. al. compared the different centrality measures e.g. the degree, the betweenness, the closeness and the eigenvector centrality as the source detection estimators [22]. Wang et. al. addressed the problem of source estimation from multiple observations under the SI model [17]. Pinto et. al. used the SI model and assumed that the direction and the times of the infection are known exactly, and solved diffusion tree problem using breadth first search from sparsely placed observers [19]. In the case of the SIR model there are two different approaches. Zhu et. al. adopted the SIR model and proposed a sample path counting approach for the source detection [18]. They proved that the source node on infinite trees minimizes the maximum distance (Jordan centrality) to the infected nodes. Lokhov et. al. used a dynamic message-passing algorithm (DMP) for the SIR model to estimate the probability that a given node produces the observed snapshot. They use a mean-field-like approximation (independence approximation) and an assumption of a tree-like contact network to compute the marginal probabilities [20]. The main contributions of the paper are the following: (i) given the non-uniqueness of finding a single epidemic source of the SIR realization on general networks, we turn the problem to finding a source probability distribution, which is a well-posed problem; (ii) we develop the analytic combinatoric and the direct Monte-Carlo approaches for determining theoretical source probability distribution and produce the benchmark solutions on the 4-connected lattice; (iii) we measure the source detectability by using the normalized Shannon entropy of the estimated source probability distribution for each of the source detection problems and observe the existence of the highly detectable and the highly undetectable regimes; (iv) using the above insights, we construct the Soft Margin epidemic source detection estimator for the arbitrary networks (static and temporal) and show that it is robust and more accurate than the state-ofthe-art approaches and much faster than the analytic combinatoric or the direct Monte-Carlo approach; (v) by using the simulations of the sexually transmitted disease (STD) model on a realistic time interval of 200 days on an empirical temporal network of sexual contacts (see the network visualization in Figure 4, plot C) we demonstrate the robustness to the uncertainty in the epidemic starting time, the network interaction orderings and in incompleteness of observations. Although we use the SIR model of epidemic spreading, our algorithms are easily applicable to other compartmental models, e.g. SI and SEIR and all other compartmental models where the states cannot be recurrent. 1 Detectability limits The main goals of this work are to better understand the nature of the epidemic source detection problem in networks, characterize its complexity, and develop efficient algorithms for estimating source probability distribution. Next, we introduce the terminology and formalize the problem. In a general case, the contactnetwork during an epidemic process can be temporal

Statistical Inference Framework for Source Detection of Contagion Processes on Arbitrary Network Structures

We introduce a statistical inference framework for maximum likelihood estimation of the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on simulations of a contagion spreading process from a set of potential sources which were infected in the observed realization. We present a number of different likelihood estimators for determining the conditional probabilities of potential initial sources producing the observed epidemic realization, which are computed in scalable and parallel way. This statistical inference framework is applicable to arbitrary networks with different dynamical spreading processes.

Epidemic centrality — is there an underestimated epidemic impact of network peripheral nodes?

AbstractIn the study of disease spreading on empirical complex networks in SIR model, initially infected nodes can be ranked according to some measure of their epidemic impact. The highest ranked nodes, also referred to as “superspreaders”, are associated to dominant epidemic risks and therefore deserve special attention. In simulations on studied empirical complex networks, it is shown that the ranking depends on the dynamical regime of the disease spreading. A possible mechanism leading to this dependence is illustrated in an analytically tractable example. In systems where the allocation of resources to counter disease spreading to individual nodes is based on their ranking, the dynamical regime of disease spreading is frequently not known before the outbreak of the disease. Therefore, we introduce a quantity called epidemic centrality as an average over all relevant regimes of disease spreading as a basis of the ranking. A recently introduced concept of phase diagram of epidemic spreading is used as a framework in which several types of averaging are studied. The epidemic centrality is compared to structural properties of nodes such as node degree, k-cores and betweenness. There is a growing trend of epidemic centrality with degree and k-cores values, but the variation of epidemic centrality is much smaller than the variation of degree or k-cores value. It is found that the epidemic centrality of the structurally peripheral nodes is of the same order of magnitude as the epidemic centrality of the structurally central nodes. The implications of these findings for the distributions of resources to counter disease spreading are discussed.

FastSIR algorithm: A fast algorithm for the simulation of the epidemic spread in large networks by using the susceptible-infected-recovered compartment model

We propose two efficient epidemic spreading algorithms (Naive SIR and FastSIR) for arbitrary network structures, based on the SIR (susceptible-infected-recovered) compartment model. The Naive SIR algorithm models full epidemic dynamics of the well-known SIR model and uses data structures efficiently to reduce running time. The FastSIR algorithm is based on the probability distribution over the number of infected nodes and uses the concept of generation time instead of explicit time in treating the spreading dynamics. Furthermore, we also propose an efficient recursive method for calculating probability distributions of the number of infected nodes. The average case running time of both algorithms has also been derived and an experimental analysis was made on five different empirical complex networks.

Phase diagram of epidemic spreading — unimodal vs. bimodal probability distributions

Disease spreading on complex networks is studied in SIR model. Simulations on empirical complex networks reveal two specific regimes of disease spreading: local containment and epidemic outbreak. The variables measuring the extent of disease spreading are in general characterized by a bimodal probability distribution. Phase diagrams of disease spreading for empirical complex networks are introduced. A theoretical model of disease spreading on m-ary tree is investigated both analytically and in simulations. It is shown that the model reproduces qualitative features of phase diagrams of disease spreading observed in empirical complex networks. The role of tree-like structure of complex networks in disease spreading is discussed.

Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multilayer Network

Personalized recommender systems rely on each user’s personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users’ rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on a multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the generated synthetic data in training recommender system models in cases when privacy policies restrict clickstream publishing.

Modeling Peer and External Influence in Online Social Networks: Case of 2013 Referendum in Croatia

In this paper we estimate the magnitude of peer and external influence on users of an opinion poll which was conducted through a Facebook application. Poll question was related to the referendum on the definition of marriage in Croatia held on \(1^{st}\) of December 2013. Through the application we collected Facebook friendship relationships, demographics and votes of over ten thousand users, as well as data on online news articles mentioning our application. Our hypothesis is that the engagement of users is driven by two main influences - one coming from within the network (peer or endogenous influence) and other coming from outside of the network (external or exogenous influence). The question is whether we can infer magnitude of these two influences using only the Facebook friendship network between users and the times of their voting. We propose a method for estimation of these two influences and demonstrate its validity on both simulated and actual data.