239,217 research outputs found
Signed Networks in Social Media
Relations between users on social media sites often reflect a mixture of
positive (friendly) and negative (antagonistic) interactions. In contrast to
the bulk of research on social networks that has focused almost exclusively on
positive interpretations of links between people, we study how the interplay
between positive and negative relationships affects the structure of on-line
social networks. We connect our analyses to theories of signed networks from
social psychology. We find that the classical theory of structural balance
tends to capture certain common patterns of interaction, but that it is also at
odds with some of the fundamental phenomena we observe --- particularly related
to the evolving, directed nature of these on-line networks. We then develop an
alternate theory of status that better explains the observed edge signs and
provides insights into the underlying social mechanisms. Our work provides one
of the first large-scale evaluations of theories of signed networks using
on-line datasets, as well as providing a perspective for reasoning about social
media sites
Signed Link Analysis in Social Media Networks
Numerous real-world relations can be represented by signed networks with
positive links (e.g., trust) and negative links (e.g., distrust). Link analysis
plays a crucial role in understanding the link formation and can advance
various tasks in social network analysis such as link prediction. The majority
of existing works on link analysis have focused on unsigned social networks.
The existence of negative links determines that properties and principles of
signed networks are substantially distinct from those of unsigned networks,
thus we need dedicated efforts on link analysis in signed social networks. In
this paper, following social theories in link analysis in unsigned networks, we
adopt three social science theories, namely Emotional Information, Diffusion of
Innovations and Individual Personality, to guide the task of link analysis in
signed networks.Comment: In the 10th International AAAI Conference on Web and Social Media
(ICWSM-16
Signed Network Modeling Based on Structural Balance Theory
The modeling of networks, specifically generative models, have been shown to
provide a plethora of information about the underlying network structures, as
well as many other benefits behind their construction. Recently there has been
a considerable increase in interest for the better understanding and modeling
of networks, but the vast majority of this work has been for unsigned networks.
However, many networks can have positive and negative links(or signed
networks), especially in online social media, and they inherently have
properties not found in unsigned networks due to the added complexity.
Specifically, the positive to negative link ratio and the distribution of
signed triangles in the networks are properties that are unique to signed
networks and would need to be explicitly modeled. This is because their
underlying dynamics are not random, but controlled by social theories, such as
Structural Balance Theory, which loosely states that users in social networks
will prefer triadic relations that involve less tension. Therefore, we propose
a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu
model for the modeling of signed networks. Our model introduces two parameters
that are able to help maintain the positive link ratio and proportion of
balanced triangles. Empirical experiments on three real-world signed networks
demonstrate the importance of designing models specific to signed networks
based on social theories to obtain better performance in maintaining signed
network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174
Network Representation Learning in Social Media
abstract: The popularity of social media has generated abundant large-scale social networks, which advances research on network analytics. Good representations of nodes in a network can facilitate many network mining tasks. The goal of network representation learning (network embedding) is to learn low-dimensional vector representations of social network nodes that capture certain properties of the networks. With the learned node representations, machine learning and data mining algorithms can be applied for network mining tasks such as link prediction and node classification. Because of its ability to learn good node representations, network representation learning is attracting increasing attention and various network embedding algorithms are proposed.
Despite the success of these network embedding methods, the majority of them are dedicated to static plain networks, i.e., networks with fixed nodes and links only; while in social media, networks can present in various formats, such as attributed networks, signed networks, dynamic networks and heterogeneous networks. These social networks contain abundant rich information to alleviate the network sparsity problem and can help learn a better network representation; while plain network embedding approaches cannot tackle such networks. For example, signed social networks can have both positive and negative links. Recent study on signed networks shows that negative links have added value in addition to positive links for many tasks such as link prediction and node classification. However, the existence of negative links challenges the principles used for plain network embedding. Thus, it is important to study signed network embedding. Furthermore, social networks can be dynamic, where new nodes and links can be introduced anytime. Dynamic networks can reveal the concept drift of a user and require efficiently updating the representation when new links or users are introduced. However, static network embedding algorithms cannot deal with dynamic networks. Therefore, it is important and challenging to propose novel algorithms for tackling different types of social networks.
In this dissertation, we investigate network representation learning in social media. In particular, we study representative social networks, which includes attributed network, signed networks, dynamic networks and document networks. We propose novel frameworks to tackle the challenges of these networks and learn representations that not only capture the network structure but also the unique properties of these social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Negative Link Prediction in Social Media
Signed network analysis has attracted increasing attention in recent years.
This is in part because research on signed network analysis suggests that
negative links have added value in the analytical process. A major impediment
in their effective use is that most social media sites do not enable users to
specify them explicitly. In other words, a gap exists between the importance of
negative links and their availability in real data sets. Therefore, it is
natural to explore whether one can predict negative links automatically from
the commonly available social network data. In this paper, we investigate the
novel problem of negative link prediction with only positive links and
content-centric interactions in social media. We make a number of important
observations about negative links, and propose a principled framework NeLP,
which can exploit positive links and content-centric interactions to predict
negative links. Our experimental results on real-world social networks
demonstrate that the proposed NeLP framework can accurately predict negative
links with positive links and content-centric interactions. Our detailed
experiments also illustrate the relative importance of various factors to the
effectiveness of the proposed framework
Discovering Polarized Communities in Signed Networks
Signed networks contain edge annotations to indicate whether each interaction
is friendly (positive edge) or antagonistic (negative edge). The model is
simple but powerful and it can capture novel and interesting structural
properties of real-world phenomena. The analysis of signed networks has many
applications from modeling discussions in social media, to mining user reviews,
and to recommending products in e-commerce sites. In this paper we consider the
problem of discovering polarized communities in signed networks. In particular,
we search for two communities (subsets of the network vertices) where within
communities there are mostly positive edges while across communities there are
mostly negative edges. We formulate this novel problem as a "discrete
eigenvector" problem, which we show to be NP-hard. We then develop two
intuitive spectral algorithms: one deterministic, and one randomized with
quality guarantee (where is the number of vertices in the
graph), tight up to constant factors. We validate our algorithms against
non-trivial baselines on real-world signed networks. Our experiments confirm
that our algorithms produce higher quality solutions, are much faster and can
scale to much larger networks than the baselines, and are able to detect
ground-truth polarized communities
Latinisation of the Kazakh Script: A Necessary Step to Reclaim Identity
Latinisation of the Kazakh alphabet is well under way. The President has signed the Law; a working version of the new script has been approved; and responsible state agencies have been appointed. However, the reform continues to generate heated debates in the media, on social networks, and in the old-fashioned offline (kitchen) conversations..
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