3,572 research outputs found
Topic-Based Influence Computation in Social Networks under Resource Constraints
As social networks are constantly changing and evolving, methods to analyze
dynamic social networks are becoming more important in understanding social
trends. However, due to the restrictions imposed by the social network service
providers, the resources available to fetch the entire contents of a social
network are typically very limited. As a result, analysis of dynamic social
network data requires maintaining an approximate copy of the social network for
each time period, locally. In this paper, we study the problem of dynamic
network and text fetching with limited probing capacities, for identifying and
maintaining influential users as the social network evolves. We propose an
algorithm to probe the relationships (required for global influence
computation) as well as posts (required for topic-based influence computation)
of a limited number of users during each probing period, based on the influence
trends and activities of the users. We infer the current network based on the
newly probed user data and the last known version of the network maintained
locally. Additionally, we propose to use link prediction methods to further
increase the accuracy of our network inference. We employ PageRank as the
metric for influence computation. We illustrate how the proposed solution
maintains accurate PageRank scores for computing global influence, and
topic-sensitive weighted PageRank scores for topic-based influence. The latter
relies on a topic-based network constructed via weights determined by semantic
analysis of posts and their sharing statistics. We evaluate the effectiveness
of our algorithms by comparing them with the true influence scores of the full
and up-to-date version of the network, using data from the micro-blogging
service Twitter. Results show that our techniques significantly outperform
baseline methods and are superior to state-of-the-art techniques from the
literature
Discovering Hidden Topical Hubs and Authorities in Online Social Networks
Finding influential users in online social networks is an important problem
with many possible useful applications. HITS and other link analysis methods,
in particular, have been often used to identify hub and authority users in web
graphs and online social networks. These works, however, have not considered
topical aspect of links in their analysis. A straightforward approach to
overcome this limitation is to first apply topic models to learn the user
topics before applying the HITS algorithm. In this paper, we instead propose a
novel topic model known as Hub and Authority Topic (HAT) model to combine the
two process so as to jointly learn the hub, authority and topical interests. We
evaluate HAT against several existing state-of-the-art methods in two aspects:
(i) modeling of topics, and (ii) link recommendation. We conduct experiments on
two real-world datasets from Twitter and Instagram. Our experiment results show
that HAT is comparable to state-of-the-art topic models in learning topics and
it outperforms the state-of-the-art in link recommendation task.Comment: Pre-print for SIAM International Conference on Data Mining (SDM'18
People are Strange when you're a Stranger: Impact and Influence of Bots on Social Networks
Bots are, for many Web and social media users, the source of many dangerous
attacks or the carrier of unwanted messages, such as spam. Nevertheless,
crawlers and software agents are a precious tool for analysts, and they are
continuously executed to collect data or to test distributed applications.
However, no one knows which is the real potential of a bot whose purpose is to
control a community, to manipulate consensus, or to influence user behavior. It
is commonly believed that the better an agent simulates human behavior in a
social network, the more it can succeed to generate an impact in that
community. We contribute to shed light on this issue through an online social
experiment aimed to study to what extent a bot with no trust, no profile, and
no aims to reproduce human behavior, can become popular and influential in a
social media. Results show that a basic social probing activity can be used to
acquire social relevance on the network and that the so-acquired popularity can
be effectively leveraged to drive users in their social connectivity choices.
We also register that our bot activity unveiled hidden social polarization
patterns in the community and triggered an emotional response of individuals
that brings to light subtle privacy hazards perceived by the user base.Comment: 10 pages, 9 figures, Proceedings of the 6th International AAAI
Conference on Weblogs and Social Media, Dublin, IR, 201
A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking
Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking
Exploratory factor analysis of graphical features for link prediction in social networks
Social Networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link prediction problem: feature-based models, Bayesian probabilistic models, probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exists three groups of features, neighborhood features, path-based features, node-based features. In this paper, we attempt to validate the underlying structure of topological features used in feature-based link prediction. The results of our analysis indicate differing results from the prevailing grouping of these features, which indicates that current literatures\u27 classification of feature groups should be redefined. Thus, the contribution of this work is exploring the factor loading of graphical features in link prediction in social networks. To the best of our knowledge, there is no prior studies had addressed it
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
The explosive growth in fake news and its erosion to democracy, justice, and
public trust has increased the demand for fake news detection and intervention.
This survey reviews and evaluates methods that can detect fake news from four
perspectives: (1) the false knowledge it carries, (2) its writing style, (3)
its propagation patterns, and (4) the credibility of its source. The survey
also highlights some potential research tasks based on the review. In
particular, we identify and detail related fundamental theories across various
disciplines to encourage interdisciplinary research on fake news. We hope this
survey can facilitate collaborative efforts among experts in computer and
information sciences, social sciences, political science, and journalism to
research fake news, where such efforts can lead to fake news detection that is
not only efficient but more importantly, explainable.Comment: ACM Computing Surveys (CSUR), 37 page
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
Randomized experiments to detect and estimate social influence in networks
Estimation of social influence in networks can be substantially biased in
observational studies due to homophily and network correlation in exposure to
exogenous events. Randomized experiments, in which the researcher intervenes in
the social system and uses randomization to determine how to do so, provide a
methodology for credibly estimating of causal effects of social behaviors. In
addition to addressing questions central to the social sciences, these
estimates can form the basis for effective marketing and public policy.
In this review, we discuss the design space of experiments to measure social
influence through combinations of interventions and randomizations. We define
an experiment as combination of (1) a target population of individuals
connected by an observed interaction network, (2) a set of treatments whereby
the researcher will intervene in the social system, (3) a randomization
strategy which maps individuals or edges to treatments, and (4) a measurement
of an outcome of interest after treatment has been assigned. We review
experiments that demonstrate potential experimental designs and we evaluate
their advantages and tradeoffs for answering different types of causal
questions about social influence. We show how randomization also provides a
basis for statistical inference when analyzing these experiments.Comment: Forthcoming in Spreading Dynamics in Social System
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