Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

GraphSE2^2: An Encrypted Graph Database for Privacy-Preserving Social Search

In this paper, we propose GraphSE$^2$, an encrypted graph database for online social network services to address massive data breaches. GraphSE$^2$ preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE$^2$ provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE$^2$ with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE$^2$ is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE$^2$: An Encrypted Graph Database for Privacy-Preserving Social Search". It includes the security proof of the proposed scheme. If you want to cite our work, please cite the conference version of i

Social Ranking Techniques for the Web

The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.Comment: 7 pages, ASONAM 201

Social network externalities and price dispersion in online markets.

Ample empirical studies in the e-commerce literature have documented that the price dispersion in online markets is 1) as large as that in offline markets, 2) persistent across time, and 3) only partially explained by observed eretailers’ attributes. Buying on the internet market is risky to consumers. First of all, consumers and the products they purchase are separated in time. There is a delay in time between the time consumers pay and the time they receive the orders. Second, consumers and the products they purchase are separated in space. Consumers cannot physically touch or examine the products at the point of purchase. As such, online markets involve an adoption process based on the interaction of consumers’ experiences in the form of references, recommendations, word of mouth, etc. The social network externalities introduced by the interaction of consumer’s experiences reduces the risk of seller choice and allows some sellers to charge higher prices for even homogeneous products. This research aims to study online market price dispersion from the social network externalities perspective. Our model posits that consumers are risk averse and assess the risk of having a satisfactory transaction from a seller based on the two dimensions of the seller’s social network externalities: quantity externality (i.e., the size of the seller’s social network) and quality externality (i.e., the satisfactory transaction probability of the seller’s social network). We further investigate the moderating effect of product value for consumers on the impact of social network externality on online market price dispersion. Our model yields several important propositions which we empirically test using data sets collected from eBay. We found that 1) both quantity externality and quality externality of social network are salient in driving online price dispersion, and 2) the salience of social network externality is stronger for purchase behavior in higher value product categories.network externalities, price dispersion, online markets, word of mouth

Modularity and community structure in networks

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.Comment: 7 pages, 3 figure

Social Networks through the Prism of Cognition

Human relations are driven by social events - people interact, exchange information, share knowledge and emotions, or gather news from mass media. These events leave traces in human memory. The initial strength of a trace depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel Cognition-driven Social Network (CogSNet) model that accounts for cognitive aspects of social perception and explicitly represents human memory dynamics. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves quality of modeling of human interactions in social networks