262 research outputs found
Policy oriented exchange networks:Was a copenhagen climate treaty possible? Scientific analysis providing new insights for agreement and a better treaty for the planet
This paper presents our predictions for the outcomes of the most controversial issues at the 15th Conference of Parties (COP) Meeting in Copenhagen, December 7-15, 2009. For these predictions we used methodology that was developed at the University of Groningen, The Netherlands, in collaboration with consultancy firm Decide (Dutch group). Based on these insights, a completely new strategy was developed, which could have resulted in a stronger treaty and could have created interests that are better harmonized among all states for a better climate and planet.</p
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Real-time classification of malicious URLs on Twitter using Machine Activity Data
Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the personâs machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. âreal-timeâ). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event â the Superbowl â and test it using data from another large sporting event â the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance
Time-aware Egocentric network-based User Profiling
International audienceImproving the egocentric network-based user's profile building process by taking into account the dynamic characteristics of social networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's egocentric network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic egocentric network-based user profiling process
Named Entity Resolution in Personal Knowledge Graphs
Entity Resolution (ER) is the problem of determining when two entities refer
to the same underlying entity. The problem has been studied for over 50 years,
and most recently, has taken on new importance in an era of large,
heterogeneous 'knowledge graphs' published on the Web and used widely in
domains as wide ranging as social media, e-commerce and search. This chapter
will discuss the specific problem of named ER in the context of personal
knowledge graphs (PKGs). We begin with a formal definition of the problem, and
the components necessary for doing high-quality and efficient ER. We also
discuss some challenges that are expected to arise for Web-scale data. Next, we
provide a brief literature review, with a special focus on how existing
techniques can potentially apply to PKGs. We conclude the chapter by covering
some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct.
2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and
applications' edited by Tiwari et a
Feature-rich networks: going beyond complex network topologies.
Abstract The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilistic Networks, and many other task-driven and data-driven models. In this paper, we propose an overview of these models and their main applications, described under the common denomination of Feature-rich Networks, i. e. models where the expressive power of the network topology is enhanced by exposing one or more peculiar features. The aim is also to sketch a scenario that can inspire the design of novel feature-rich network models, which in turn can support innovative methods able to exploit the full potential of mining complex network structures in domain-specific applications
Efficient Sampling Algorithms for Approximate Motif Counting in Temporal Graph Streams
A great variety of complex systems, from user interactions in communication
networks to transactions in financial markets, can be modeled as temporal
graphs consisting of a set of vertices and a series of timestamped and directed
edges. Temporal motifs are generalized from subgraph patterns in static graphs
which consider edge orderings and durations in addition to topologies. Counting
the number of occurrences of temporal motifs is a fundamental problem for
temporal network analysis. However, existing methods either cannot support
temporal motifs or suffer from performance issues. Moreover, they cannot work
in the streaming model where edges are observed incrementally over time. In
this paper, we focus on approximate temporal motif counting via random
sampling. We first propose two sampling algorithms for temporal motif counting
in the offline setting. The first is an edge sampling (ES) algorithm for
estimating the number of instances of any temporal motif. The second is an
improved edge-wedge sampling (EWS) algorithm that hybridizes edge sampling with
wedge sampling for counting temporal motifs with vertices and edges.
Furthermore, we propose two algorithms to count temporal motifs incrementally
in temporal graph streams by extending the ES and EWS algorithms referred to as
SES and SEWS. We provide comprehensive analyses of the theoretical bounds and
complexities of our proposed algorithms. Finally, we perform extensive
experimental evaluations of our proposed algorithms on several real-world
temporal graphs. The results show that ES and EWS have higher efficiency,
better accuracy, and greater scalability than state-of-the-art sampling methods
for temporal motif counting in the offline setting. Moreover, SES and SEWS
achieve up to three orders of magnitude speedups over ES and EWS while having
comparable estimation errors for temporal motif counting in the streaming
setting.Comment: 27 pages, 11 figures; overlapped with arXiv:2007.1402
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
Tweet sentiment: From classification to quantification
AbstractâSentiment classification has become a ubiq-uitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet senti-ment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the rel-ative frequency (a.k.a. âprevalenceâ) of the different classes in the dataset. The latter task is called quan-tification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluationmeasures different from those used for classification. In this paper we show, on a multiplicity of TSC datasets, that using a quantification-specific algorithm produces substan-tially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers inter-ested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluationmeasures. 1
- âŠ