290 research outputs found
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
We propose a novel class of network models for temporal dyadic interaction
data. Our goal is to capture a number of important features often observed in
social interactions: sparsity, degree heterogeneity, community structure and
reciprocity. We propose a family of models based on self-exciting Hawkes point
processes in which events depend on the history of the process. The key
component is the conditional intensity function of the Hawkes Process, which
captures the fact that interactions may arise as a response to past
interactions (reciprocity), or due to shared interests between individuals
(community structure). In order to capture the sparsity and degree
heterogeneity, the base (non time dependent) part of the intensity function
builds on compound random measures following Todeschini et al. (2016). We
conduct experiments on a variety of real-world temporal interaction data and
show that the proposed model outperforms many competing approaches for link
prediction, and leads to interpretable parameters
Mesoscopic structure and social aspects of human mobility
The individual movements of large numbers of people are important in many
contexts, from urban planning to disease spreading. Datasets that capture human
mobility are now available and many interesting features have been discovered,
including the ultra-slow spatial growth of individual mobility. However, the
detailed substructures and spatiotemporal flows of mobility - the sets and
sequences of visited locations - have not been well studied. We show that
individual mobility is dominated by small groups of frequently visited,
dynamically close locations, forming primary "habitats" capturing typical daily
activity, along with subsidiary habitats representing additional travel. These
habitats do not correspond to typical contexts such as home or work. The
temporal evolution of mobility within habitats, which constitutes most motion,
is universal across habitats and exhibits scaling patterns both distinct from
all previous observations and unpredicted by current models. The delay to enter
subsidiary habitats is a primary factor in the spatiotemporal growth of human
travel. Interestingly, habitats correlate with non-mobility dynamics such as
communication activity, implying that habitats may influence processes such as
information spreading and revealing new connections between human mobility and
social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table
(supporting information
A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament
We analyse bill cosponsorship networks in the Italian Chamber of Deputies. In comparison with other parliaments, a distinguishing feature of the Chamber is the large number of political groups. Our analysis aims to infer the pattern of collaborations between these groups from data on bill cosponsorships. We propose an extension of stochastic block models for edge-valued graphs and derive measures of group productivity and of collaboration between political parties. As the model proposed encloses a large number of parameters, we pursue a penalized likelihood approach that enables us to infer a sparse reduced graph displaying collaborations between political parties
Temporal patterns of reciprocity in communication networks
Human communication, the essence of collective social phenomena ranging from small-scale organizations to worldwide online platforms, features intense reciprocal interactions between members in order to achieve stability, cohesion, and cooperation in social networks. While high levels of reciprocity are well known in aggregated communication data, temporal patterns of reciprocal information exchange have received far less attention. Here we propose measures of reciprocity based on the time ordering of interactions and explore them in data from multiple communication channels, including calls, messaging and social media. By separating each channel into reciprocal and non-reciprocal temporal networks, we find persistent trends that point to the distinct roles of one-to-one exchange versus information broadcast. We implement several null models of communication activity, which identify memory, a higher tendency to repeat interactions with past contacts, as a key source of temporal reciprocity. When adding memory to a model of activity-driven, time-varying networks, we reproduce the levels of temporal reciprocity seen in empirical data. Our work adds to the theoretical understanding of the emergence of reciprocity in human communication systems, hinting at the mechanisms behind the formation of norms in social exchange and large-scale cooperation.publishedVersionPeer reviewe
A semiparametric extension of the stochastic block model for longitudinal networks
To model recurrent interaction events in continuous time, an extension of the
stochastic block model is proposed where every individual belongs to a latent
group and interactions between two individuals follow a conditional
inhomogeneous Poisson process with intensity driven by the individuals' latent
groups. The model is shown to be identifiable and its estimation is based on a
semiparametric variational expectation-maximization algorithm. Two versions of
the method are developed, using either a nonparametric histogram approach (with
an adaptive choice of the partition size) or kernel intensity estimators. The
number of latent groups can be selected by an integrated classification
likelihood criterion. Finally, we demonstrate the performance of our procedure
on synthetic experiments, analyse two datasets to illustrate the utility of our
approach and comment on competing methods
A Novel Methodology for designing Policies in Mobile Crowdsensing Systems
Mobile crowdsensing is a people-centric sensing system based on users'
contributions and incentive mechanisms aim at stimulating them. In our work, we
have rethought the design of incentive mechanisms through a game-theoretic
methodology. Thus, we have introduced a multi-layer social sensing framework,
where humans as social sensors interact on multiple social layers and various
services. We have proposed to weigh these dynamic interactions by including the
concept of homophily and we have modelled the evolutionary dynamics of sensing
behaviours by defining a mathematical framework based on multiplex EGT,
quantifying the impact of homophily, network heterogeneity and various social
dilemmas. We have detected the configurations of social dilemmas and network
structures that lead to the emergence and sustainability of human cooperation.
Moreover, we have defined and evaluated local and global Nash equilibrium
points by including the concepts of homophily and heterogeneity. We have
analytically defined and measured novel statistical measures of social honesty,
QoI and users' behavioural reputation scores based on the evolutionary
dynamics. We have defined the Decision Support System and a novel incentive
mechanism by operating on the policies in terms of users' reputation scores,
that also incorporate users' behaviours other than quality and quantity of
contributions. Experimentally, we have considered the Waze dataset on vehicular
traffic monitoring application and derived the disbursement of incentives
comparing our method with baselines. Results demonstrate that our methodology,
which also includes the local (microscopic) spatio-temporal distribution of
behaviours, is able to better discriminate users' behaviours. This multi-scale
characterisation of users represents a novel research direction and paves the
way for novel policies on mobile crowdsensing systems
- …