3,973 research outputs found
An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media
Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Sequences of purchases in credit card data reveal life styles in urban populations
Zipf-like distributions characterize a wide set of phenomena in physics,
biology, economics and social sciences. In human activities, Zipf-laws describe
for example the frequency of words appearance in a text or the purchases types
in shopping patterns. In the latter, the uneven distribution of transaction
types is bound with the temporal sequences of purchases of individual choices.
In this work, we define a framework using a text compression technique on the
sequences of credit card purchases to detect ubiquitous patterns of collective
behavior. Clustering the consumers by their similarity in purchases sequences,
we detect five consumer groups. Remarkably, post checking, individuals in each
group are also similar in their age, total expenditure, gender, and the
diversity of their social and mobility networks extracted by their mobile phone
records. By properly deconstructing transaction data with Zipf-like
distributions, this method uncovers sets of significant sequences that reveal
insights on collective human behavior.Comment: 30 pages, 26 figure
Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs
Influence Analysis is one of the well-known areas of Social Network Analysis. However, discovering influencers from micro-blog networks based on topics has gained recent popularity due to its specificity. Besides, these data networks are massive, continuous and evolving. Therefore, to address the above challenges we propose a dynamic framework for topic modelling and identifying influencers in the same process. It incorporates dynamic sampling, community detection and network statistics over graph data stream from a social media activity management application. Further, we compare the graph measures against each other empirically and observe that there is no evidence of correlation between the sets of users having large number of friends and the users whose posts achieve high acceptance (i.e., highly liked, commented and shared posts). Therefore, we propose a novel approach that incorporates a user's reachability and also acceptability by other users. Consequently, we improve on graph metrics by including a dynamic acceptance score (integrating content quality with network structure) for ranking influencers in micro-blogs. Additionally, we analysed the topic clusters' structure and quality with empirical experiments and visualization.Fundaçao para a Ciência e a Tecnologia, Grant/Award Number: UIDB/50014/202
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
Causal schema induction for knowledge discovery
Making sense of familiar yet new situations typically involves making
generalizations about causal schemas, stories that help humans reason about
event sequences. Reasoning about events includes identifying cause and effect
relations shared across event instances, a process we refer to as causal schema
induction. Statistical schema induction systems may leverage structural
knowledge encoded in discourse or the causal graphs associated with event
meaning, however resources to study such causal structure are few in number and
limited in size. In this work, we investigate how to apply schema induction
models to the task of knowledge discovery for enhanced search of
English-language news texts. To tackle the problem of data scarcity, we present
Torquestra, a manually curated dataset of text-graph-schema units integrating
temporal, event, and causal structures. We benchmark our dataset on three
knowledge discovery tasks, building and evaluating models for each. Results
show that systems that harness causal structure are effective at identifying
texts sharing similar causal meaning components rather than relying on lexical
cues alone. We make our dataset and models available for research purposes.Comment: 8 pages, appendi
Analysis of Protein-Protein Interaction Networks Using High Performance Scalable Tools
Protein-Protein Interaction (PPI) Research currently generates an extraordinary amount of publications and interest in fellow computer scientists and biologists alike because of the underlying potential of the source material that researchers can work with. PPI networks are the networks of protein complexes formed by biochemical events or electrostatic forces serving a biological function [1]. Since the analysis of the protein networks is now growing, we have more information regarding protein, genomes and their influence on life. Today, PPI networks are used to study diseases, improve drugs and understand other processes in medicine and health that will eventually help mankind.
Though PPI network research is considered extremely important in the field, there is an issue – we do not have enough people who have enough interdisciplinary knowledge in both the fields of biology and computer science; this limits our rate of progress in the field.
Most biologists that are not expert coders need a way of calculating graph values and information that will help them analyze the graphs better without having to manipulate the data themselves. In this research, I test a few ways of achieving results through the use of available frameworks and algorithms, present the results and compare each method’s efficacy.
My analysis takes place on very large datasets where I calculate several centralities and other data from the graph using different metrics, and I also visualize them in order to gain further insight. I also managed to note the significance of MPI and multithreading on the results thus obtained that suggest building scalable tools will help improve the analysis immensely
Identify Multiple Types of Social Influences on Smart Contract Adoption in Blockchain User Network: An Empirical Examination of CryptoKitties in Ethereum
Smart contract brings more versatile functions in blockchain technology. However, its adoption rate is not as high as expected. Currently, there is no thorough study addressing such problem. To fill such gap, we propose to use peer influence to explain smart contract adoption in blockchain user network. We explore whether and how multiple types of peer influence including direct pee influence and indirect peer influence, simultaneously affect individual adoption decisions of smart contracts. Our hypotheses are examined in the context of CryptoKitties adoption in the Ethereum network using the public dataset of Ethereum including 350 million transactions from over 20 million distinct accounts. Our results suggest that the adoption of the software is positively affected by direct peer influence and indirect peer influence. Moreover, we find that users who have higher social status in the blockchain network are less susceptible to peer influence. The results provide strong evidence of peer influence on smart contract adoption through various mechanisms
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