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Engineering Social Contagions: Optimal Network Seeding in the Presence of Homophily by Sinan Aral, Lev Muchnik, Arun Sundararajan :: SSRN

Engineering Social Contagions: Optimal Network Seeding in the Presence of Homophily by Sinan Aral, Lev Muchnik, Arun Sundararajan :: SSRN | Social Network Analysis #sna | Scoop.it

We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product

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We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations.


First, failure to consider homophily creates significant overestimation of the effectiveness of seeding strategies, casting doubt on conclusions drawn by simulation studies that do not model homophily.


Second, seeding is constrained by the small fraction of potential influencers that exist in the network. We find that seeding more than 0.2% of the population is wasteful because the gain from their adoption is lower than the gain from their natural adoption (without seeding).


Third, seeding is more effective in the presence of greater social influence. Stronger peer influence creates a greater than additive effect when combined with seeding. Our findings call into question some conventional wisdom about these strategies and suggest that their overall effectiveness may be overestimated.

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Social Network Analysis #sna
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Using Social network analysis measures

Using Social network analysis measures | Social Network Analysis #sna | Scoop.it
Using network visualisation and SNA measures, our intern dissects the connections in the Enron corpus to uncover management structures and play detective!
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Efficiently identifying critical nodes in large complex networks

The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined value k. Existing optimization algorithms are incapable of finding a good set R in graphs with many thousands or millions of vertices due to the associated computational cost. Hence, there exists a need for a time- and space-efficient approach for evaluating the impact of removing any v∈V in the context of the CNDP. In this paper, we propose an algorithm based on a modified depth-first search that requires O(k(|V|+|E|)) time complexity. We employ the method within in a greedy algorithm for quickly identifying R. Our experimental results consider small- (≤250 nodes) and medium-sized (≤25,000 nodes) networks, where it is possible to compare to known optimal solutions or results obtained by other heuristics. Additionally, we show results using six real-world networks. The proposed algorithm can be easily extended to vertex- and edge-weighted variants of the CNDP.
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Computational Social Networks : Long-range degree correlations in complex networks

Social networks are often degree correlated between nearest neighbors, an effect termed homophily, wherein individuals connect to nearest neighbors of similar connectivity. Whether friendships or other associations are so correlated beyond the first-neighbors, and whether such correlations are an inherent property of the network or are dependent on other specifics of social interactions, remains unclear. 

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Here we address these problems by examining long-range degree correlations in three undirected online social and three undirected nonsocial (airport, transcriptional-regulatory) networks. Degree correlations were measured using Pearson correlation scores and by calculating the average neighbor degrees for nodes separated by up to 5 sequential links. We found that the online social networks exhibited primarily weak anticorrelation at the first-neighbor level, and tended more strongly towards disassortativity as separation distances increased. In contrast, the nonsocial networks were disassortative among first-neighbors, but exhibited assortativity at longer separation distances. In addition, the average degrees of the separated neighbors approached the average network connectivity after approximately 3-4 steps. Finally, we observed that two algorithms designed to grow networks on a node-by-node basis failed to reproduce all the correlative features representative of the social networks studied here.

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Ecology 2.0: Coexistence and domination among interacting networks

The overwhelming success of the web 2.0, with online social networks as key actors, has induced a paradigm shift in the nature of human interactions. 

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[Network analysis] Digital Humanities on Twitter, a small-world?

[Network analysis] Digital Humanities on Twitter, a small-world? | Social Network Analysis #sna | Scoop.it
Digital humanities, Data visualization, Network analysis
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Visualize Your Graph with RNeo4j and visNetwork #rstats #sna

Someone recently posted an issue on RNeo4j where they needed help visualizing their graph in visNetwork, which has turned out to be a pretty fun R package. I decided to turn my answer into a blog post.

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Network Under Joint Node and Link Attacks Vulnerability Assessment Methods and Analysis

“Network Under Joint Node and Link Attacks Vulnerability Assessment Methods and Analysis
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Natural Language Processing With Neo4j - Mining Paradigmatic Word Associations · William Lyon

Natural Language Processing With Neo4j - Mining Paradigmatic Word Associations · William Lyon | Social Network Analysis #sna | Scoop.it
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This is what a graph of 8,000 fake Twitter accounts looks like

This is what a graph of 8,000 fake Twitter accounts looks like | Social Network Analysis #sna | Scoop.it
Recently I've been plagued with Tweets saying that I'm "trending in London."As flattering as that is, it's not true. There appears to be a network of Twitter bots which are randomly

Via João Greno Brogueira
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The Social Contagion of Generosity

The Social Contagion of Generosity | Social Network Analysis #sna | Scoop.it
Why do people help strangers when there is a low probability that help will be directly reciprocated or socially rewarded? A possible explanation is that these acts are contagious: those who receive or observe help from a stranger become more likely to help others. We test two mechanisms for the social contagion of generosity among strangers: generalized reciprocity (a recipient of generosity is more likely to pay it forward) and third-party influence (an observer of generous behavior is more l
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Analyzing R-Bloggers’ posts via Twitter

Analyzing R-Bloggers’ posts via Twitter | Social Network Analysis #sna | Scoop.it
For those who don’t know, every time a new blog post gets added to R-Bloggers, it gets a corresponding tweet by @Rbloggers, which gets seen by Rbloggers’ ~20k followers fairly fast. And every time my post gets published, I can’t help but check up on how many people gave that tweet some Twitter love, ie. “favorite”d or “retweet”ed it. It’s even more exciting than getting a Facebook “like” on a photo from Costa Rica!

Seeing all these tweets and how some tweets get much more attention than others
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Computational Social Networks: Handling big data of online social networks on a small machine

Dealing with big data in computational social networks may require powerful machines, big storage, and high bandwidth, which may seem beyond the capacity of small labs. We demonstrate that researchers with limited resources may still be able to conduct big-data research by focusing on a specific type of data. In particular, we present a system called MPT (Microblog Processing Toolkit) for handling big volume of microblog posts with commodity computers, which can handle tens of millions of micro posts a day. MPT supports fast search on multiple keywords and returns statistical results. We describe in this paper the architecture of MPT for data collection and phrase search for returning search results with statistical analysis. We then present different indexing mechanisms and compare them on the microblog posts we collected from popular online social network sites in mainland China.
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Computational Social Networks - a Springer Open journal

Computational Social Networks - a Springer Open journal | Social Network Analysis #sna | Scoop.it

mathematical aspects, and applications of social computing

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Focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media

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The Net Effect: Using social media data to understand the impact of a conference on social networks

The research uses social media data from Twitter to develop a methodology for understanding the effects of events and conferences.
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Keys to Developing Leaders When Your Team Grows Too Big

Keys to Developing Leaders When Your Team Grows Too Big | Social Network Analysis #sna | Scoop.it
When your team grows too big, it's time to consider developing leaders. We cover the keys to developing leaders and effectively managing your growing team.
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Analysing the Twitter Mentions Network

Analysing the Twitter Mentions Network | Social Network Analysis #sna | Scoop.it
By Douglas Ashton, Consultant One of the big successes of data analytics is the cultural change in how business decisions are being made. There is now wide spread acceptance of the role that data science has to play in decision making.
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Social Network Models and Data, EC'15 Tutorial

Social networks underly a broad range of social and economic research questions that are increasingly being understood through large-scale computational analyses. In particular, the study of social influence and information diffusion on social networks have rich modeling histories, while opportunities in online instrumentation and experimentations are now providing tremendous advances in our abilities for theory testing as well as theory development.
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 This tutorial will provide a brief overview of models of social networks and social influence, and then focus on giving an overview of recent evidence for how these processes behave empirically in diverse online settings. A particular emphasis will be placed on efforts to approach these problems through causal inference, moving beyond "big data" to "big experimentation".

 

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Modeling Ebola Contagion Using Airline Networks in R

Modeling Ebola Contagion Using Airline Networks in R | Social Network Analysis #sna | Scoop.it
I first became interested in networks when reading Matthew O'Jackson's 2010 paper describing their application to economics. During the 2014 ebola outbreak, there was a lot of concern over the dise...
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Static and dynamic network visualization with R

Static and dynamic network visualization with R | Social Network Analysis #sna | Scoop.it
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ukituki's curator insight, June 20, 7:18 AM

Fantastic tutorial on networks visualization in R by @Ognyanova

#rstats

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Network Analysis in Systems Biology Bonus Lecture Agent Based Modeling with NetLogo

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. PCA ...
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Complex network study of Brazilian soccer players

Complex network study of Brazilian soccer players | Social Network Analysis #sna | Scoop.it
Although being a very popular sport in many countries, soccer has not received much attention from the scientific community. In this paper, we study soccer from a complex network point of view. First, we consider a bipartite network with two kinds of vertices or nodes: the soccer players and the clubs. Real data were gathered from the 32 editions of the Brazilian soccer championship, in a total of $13\phantom{\rule{0.2em}{0ex}}411$ soccer players and 127 clubs. We find a lot of interesting and p
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