Exploring the Social Networks of Online Investors

Online trading has now become the prevalent form of stock market investing. With a large percentage of investors now online, understanding how they make use of social media to share and gather information is of major interest to service providers, regulators, researchers and to investors themselves. Previous studies have explored public online investor networks such as stock forums, but few studies have investigated investors’ private online social networks. This paper reports on a qualitative study that interviewed 26 online investors about the various forms of social networks they participate in. Some online investors continue to rely on their private, offline networks of family and friends for information and advice. Other investors have taken their social networks online, but these online networks are still private. While investors do participate in public stock forums, it was also found that private social networks exist within these public virtual communities. Forum members get to know other members outside of the forums and form personal relationships. Such private social networks are not visible to outsiders

Differentially Private Exponential Random Graphs

We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, and thus offer rigorous privacy guarantees. More specifically, we use the randomized response mechanism to release networks under $\epsilon$-edge differential privacy. To maintain utility for statistical inference, treating the original graph as missing, we propose a way to use likelihood based inference and Markov chain Monte Carlo (MCMC) techniques to fit ERGMs to the produced synthetic networks. We demonstrate the usefulness of the proposed techniques on a real data example.Comment: minor edit

Lawlessness and Economics: Alternative Modes of Governance

How can property rights be protected and contracts be enforced in countries where the rule of law is ineffective or absent? How can firms from advanced market economies do business in such circumstances? In Lawlessness and Economics , Avinash Dixit examines the theory of private institutions that transcend or supplement weak economic governance from the state. In much of the world and through much of history, private mechanisms--such as long-term relationships, arbitration, social networks to disseminate information and norms to impose sanctions, and for-profit enforcement services--have grown up in place of formal, state-governed institutions. Even in countries with strong legal systems, many of these mechanisms continue under the shadow of the law. Numerous case studies and empirical investigations have demonstrated the variety, importance, and merits, and drawbacks of such institutions. This book builds on these studies and constructs a toolkit of theoretical models to analyze them. The models shed new conceptual light on the different modes of governance, and deepen our understanding of the interaction of the alternative institutions with each other and with the government's law. For example, one model explains the limit on the size of social networks and illuminates problems in the transition to more formal legal systems as economies grow beyond this limit. Other models explain why for-profit enforcement is inefficient. The models also help us understand why state law dovetails with some non-state institutions and collides with others. This can help less-developed countries and transition economies devise better processes for the introduction or reform of their formal legal systems.property rights, contracts, law, business, economic governance, private mechanisms, arbitration, social networks, norms, sanctions, reform

Social capital

Like other forms of capital, such as financial capital, social capital refers to the available resources upon which an individual or a community can draw. In the case of social capital, the resources are formed by people\u27s networks and relationships. The two main theorists of social capital as applied to education are James Coleman and Robert Putnam. Coleman\u27s work stems from his 1966 report, which indicated that family background accounts for student achievement more than variations in schools do. His subsequent work comparing the greater success of private school students to that of public school students expanded the role of family background to include all the social resources available to a student. The greater success of students in religious private schools was attributable to the overlapping, cross-generational social networks provided by the partnerships among families, church, and school. Coleman defined these partnerships as social capital, which pertained to the norms, the social networks, and the relationships between adults and children that were of value for the child\u27s growing up. Social capital existed within the family but also outside the family, in the community. Within the family, social capital depended upon the strength of parents\u27 relationships with their children. Coleman f~mnd, for example, that even when controlling for parents\u27 socioeconomic status, dropout rates for high school students were lowest when there were two parents, only one sibling to share parental attention, and mothers who expected their children to attend college. Thus, a family\u27s social capital increases children\u27s socioeconomic success, or the children\u27s human capital. Outside of the family, social capital depends upon people\u27s sense of obligation and reciprocity, like quid pro quo, within a community

Stealing Links from Graph Neural Networks

Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data contains sensitive information, e.g., social relationships. Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs). Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection. In this work, we propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph. Specifically, given a black-box access to a GNN model, our attacks can infer whether there exists a link between any pair of nodes in the graph used to train the model. We call our attacks link stealing attacks. We propose a threat model to systematically characterize an adversary's background knowledge along three dimensions which in total leads to a comprehensive taxonomy of 8 different link stealing attacks. We propose multiple novel methods to realize these 8 attacks. Extensive experiments on 8 real-world datasets show that our attacks are effective at stealing links, e.g., AUC (area under the ROC curve) is above 0.95 in multiple cases. Our results indicate that the outputs of a GNN model reveal rich information about the structure of the graph used to train the model.Comment: To appear in the 30th Usenix Security Symposium, August 2021, Vancouver, B.C., Canad

Not the "other" mother : how language constructs lesbian co-parenting relationships

This paper presents findings from recent Australian qualitative research with lesbian co-parents where study participants\u27 fluid narrative identities are&nbsp; deconstructed in order to better understand how language constructs relationships within private and public domains. Language used to define, describe and give meaning to roles and relationships of lesbian co-parents within social and kinship networks and wider community is explored. Through claiming language and telling their stories lesbian co-parents give meaning to their lives; affirm their identity; and present their relationships as visible and valid. <br /

Preserve data-while-sharing: An Efficient Technique for Privacy Preserving in OSNs

Online Social Networks (OSNs) have become one of the major platforms for social interactions, such as building up relationships, sharing personal experiences, and providing other services. Rapid growth in Social Network has attracted various groups like the scientific community and business enterprise to use these huge social network data to serve their various purposes. The process of disseminating extensive datasets from online social networks for the purpose of conducting diverse trend analyses gives rise to apprehensions regarding privacy, owing to the disclosure of personal information disclosed on these platforms. Privacy control features have been implemented in widely used online social networks (OSNs) to empower users in regulating access to their personal information. Even if Online Social Network owners allow their users to set customizable privacy, attackers can still find out users’ private information by finding the relationships between public and private information with some background knowledge and this is termed as inference attack. In order to defend against these inference attacks this research work could completely anonymize the user identity. This research work designs an optimization algorithm that aims to strike a balance between self-disclosure utility and their privacy. This research work proposes two privacy preserving algorithms to defend against an inference attack. The research work design an Privacy-Preserving Algorithm (PPA) algorithm which helps to achieve high utility by allowing users to share their data with utmost privacy. Another algorithm-Multi-dimensional Knapsack based Relation Disclosure Algorithm (mdKP-RDA) that deals with social relation disclosure problems with low computational complexity. The proposed work is evaluated to test the effectiveness on datasets taken from actual social networks. According on the experimental results, the proposed methods outperform the current methods. &nbsp