109,405 research outputs found
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
Multirelational Organization of Large-scale Social Networks in an Online World
The capacity to collect fingerprints of individuals in online media has
revolutionized the way researchers explore human society. Social systems can be
seen as a non-linear superposition of a multitude of complex social networks,
where nodes represent individuals and links capture a variety of different
social relations. Much emphasis has been put on the network topology of social
interactions, however, the multi-dimensional nature of these interactions has
largely been ignored in empirical studies, mostly because of lack of data.
Here, for the first time, we analyze a complete, multi-relational, large social
network of a society consisting of the 300,000 odd players of a massive
multiplayer online game. We extract networks of six different types of
one-to-one interactions between the players. Three of them carry a positive
connotation (friendship, communication, trade), three a negative (enmity, armed
aggression, punishment). We first analyze these types of networks as separate
entities and find that negative interactions differ from positive interactions
by their lower reciprocity, weaker clustering and fatter-tail degree
distribution. We then proceed to explore how the inter-dependence of different
network types determines the organization of the social system. In particular
we study correlations and overlap between different types of links and
demonstrate the tendency of individuals to play different roles in different
networks. As a demonstration of the power of the approach we present the first
empirical large-scale verification of the long-standing structural balance
theory, by focusing on the specific multiplex network of friendship and enmity
relations.Comment: 7 pages, 5 figures, accepted for publication in PNA
Analyses of a Virtual World
We present an overview of a series of results obtained from the analysis of
human behavior in a virtual environment. We focus on the massive multiplayer
online game (MMOG) Pardus which has a worldwide participant base of more than
400,000 registered players. We provide evidence for striking statistical
similarities between social structures and human-action dynamics in the real
and virtual worlds. In this sense MMOGs provide an extraordinary way for
accurate and falsifiable studies of social phenomena. We further discuss
possibilities to apply methods and concepts developed in the course of these
studies to analyse oral and written narratives.Comment: 16 pages, 7 figures. To appear in: "Maths Meets Myths:
Complexity-science approaches to folktales, myths, sagas, and histories."
Editors: R. Kenna, M. Mac Carron, P. Mac Carron. (Springer, 2016
Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses
Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of
Digital Information & Web Technologies (ICADIWT), India, February 2014 (6
pages, 3 figures
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Spreading in Social Systems: Reflections
In this final chapter, we consider the state-of-the-art for spreading in
social systems and discuss the future of the field. As part of this reflection,
we identify a set of key challenges ahead. The challenges include the following
questions: how can we improve the quality, quantity, extent, and accessibility
of datasets? How can we extract more information from limited datasets? How can
we take individual cognition and decision making processes into account? How
can we incorporate other complexity of the real contagion processes? Finally,
how can we translate research into positive real-world impact? In the
following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems";
Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur
The structure of borders in a small world
Geographic borders are not only essential for the effective functioning of
government, the distribution of administrative responsibilities and the
allocation of public resources, they also influence the interregional flow of
information, cross-border trade operations, the diffusion of innovation and
technology, and the spatial spread of infectious diseases. However, as growing
interactions and mobility across long distances, cultural, and political
borders continue to amplify the small world effect and effectively decrease the
relative importance of local interactions, it is difficult to assess the
location and structure of effective borders that may play the most significant
role in mobility-driven processes. The paradigm of spatially coherent
communities may no longer be a plausible one, and it is unclear what structures
emerge from the interplay of interactions and activities across spatial scales.
Here we analyse a multi-scale proxy network for human mobility that
incorporates travel across a few to a few thousand kilometres. We determine an
effective system of geographically continuous borders implicitly encoded in
multi-scale mobility patterns. We find that effective large scale boundaries
define spatially coherent subdivisions and only partially coincide with
administrative borders. We find that spatial coherence is partially lost if
only long range traffic is taken into account and show that prevalent models
for multi-scale mobility networks cannot account for the observed patterns.
These results will allow for new types of quantitative, comparative analyses of
multi-scale interaction networks in general and may provide insight into a
multitude of spatiotemporal phenomena generated by human activity.Comment: 9 page
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