PHDP: Preserving Persistent Homology in Differentially Private Graph Publications

Online social networks (OSNs) routinely share and analyze user data. This requires protection of sensitive user information. Researchers have proposed several techniques to anonymize the data of OSNs. Some differential-privacy techniques claim to preserve graph utility under certain graph metrics, as well as guarantee strict privacy. However, each graph utility metric reveals the whole graph in specific aspects.We employ persistent homology to give a comprehensive description of the graph utility in OSNs. This paper proposes a novel anonymization scheme, called PHDP, which preserves persistent homology and satisfies differential privacy. To strengthen privacy protection, we add exponential noise to the adjacency matrix of the network and find the number of adding/deleting edges. To maintain persistent homology, we collect edges along persistent structures and avoid perturbation on these edges. Our regeneration algorithms balance persistent homology with differential privacy, publishing an anonymized graph with a guarantee of both. Evaluation result show that the PHDP-anonymized graph achieves high graph utility, both in graph metrics and application metrics

A theoretical analysis of the relationship between social capital and corporate social responsibility: concepts and definitions

The paper studies the relationship between social capital (SC) and Corporate Social Responsibility (CSR) by investigating the idea of a virtuous circle, between the level of SC and the implementation of CSR practices, that fosters socio-economic development by generating social inclusion and social networks based on trust and trustworthiness. Following the literature on SC that stresses its multidimensional character, both a cognitive and a structural idea of SC are considered. The first one essentially refers to the dispositional characters of agents that affect their propensity to behave in different ways. The latter refers to social networks connecting agents. With regard to the concept of CSR, a contractarian approach is adopted and CSR is considered as an extended model of corporate governance, based on the fiduciary duties owed to all the firm’s stakeholders. Among stakeholders, a original distinction between “strong” and “weak” stakeholders is introduced. The key element that allows to distinguish between strong and weak stakeholders concerns the consequences that the break in the relationship with the firm produces both on the stakeholder and on the firm. Both these two categories have made specific investments in the firm. However, strong stakeholders are precious for the firm because they bring in strategic assets. On the contrary, weak stakeholders do not bring strategic assets into the firm and firms have material incentives at defecting in the relationship with them. Considering the notions of cognitive and structural SC, a contractarian approach to CSR and the distinction between weak and strong stakeholders, the paper shows that: a) the level of cognitive SC plays a key role in inducing the firm to adopt and observe CSR practices that respect all the stakeholders; b) the decision of adopting formal instruments of CSR contributes to create cognitive SC that is endogenously determined in the model; c) the level of cognitive SC and the decision of adopting CSR practices creates structural SC in terms of a long term relationship between the firm and the weak and strong stakeholders.Social capital, Corporate Social Responsibility, Social network, Ideal utility, Cooperation, Trust.

Reducing Access Disparities in Networks using Edge Augmentation

In social networks, a node's position is a form of \it{social capital}. Better-positioned members not only benefit from (faster) access to diverse information, but innately have more potential influence on information spread. Structural biases often arise from network formation, and can lead to significant disparities in information access based on position. Further, processes such as link recommendation can exacerbate this inequality by relying on network structure to augment connectivity. We argue that one can understand and quantify this social capital through the lens of information flow in the network. We consider the setting where all nodes may be sources of distinct information, and a node's (dis)advantage deems its ability to access all information available on the network. We introduce three new measures of advantage (broadcast, influence, and control), which are quantified in terms of position in the network using \it{access signatures} -- vectors that represent a node's ability to share information. We then consider the problem of improving equity by making interventions to increase the access of the least-advantaged nodes. We argue that edge augmentation is most appropriate for mitigating bias in the network structure, and frame a budgeted intervention problem for maximizing minimum pairwise access. Finally, we propose heuristic strategies for selecting edge augmentations and empirically evaluate their performance on a corpus of real-world social networks. We demonstrate that a small number of interventions significantly increase the broadcast measure of access for the least-advantaged nodes (over 5 times more than random), and also improve the minimum influence. Additional analysis shows that these interventions can also dramatically shrink the gap in advantage between nodes (over \%82) and reduce disparities between their access signatures

Learning in Dynamic Inter-firm Networks - The Efficacy of Multiple Contacts

This paper examines the relevance of both an efficiency-based network strategy and a learning-based network strategy in the context of inter-firm partnering. The effect of these different forms of network behaviour on company performance is analysed for companies in the international computer industry. Strategies associated with learning through so-called exploratory networks appear to generate a greater impact on technological performance in a dynamic environment than efficiency strategies through exploitative networks.industrial organization ;

Structure of Heterogeneous Networks

Heterogeneous networks play a key role in the evolution of communities and the decisions individuals make. These networks link different types of entities, for example, people and the events they attend. Network analysis algorithms usually project such networks unto simple graphs composed of entities of a single type. In the process, they conflate relations between entities of different types and loose important structural information. We develop a mathematical framework that can be used to compactly represent and analyze heterogeneous networks that combine multiple entity and link types. We generalize Bonacich centrality, which measures connectivity between nodes by the number of paths between them, to heterogeneous networks and use this measure to study network structure. Specifically, we extend the popular modularity-maximization method for community detection to use this centrality metric. We also rank nodes based on their connectivity to other nodes. One advantage of this centrality metric is that it has a tunable parameter we can use to set the length scale of interactions. By studying how rankings change with this parameter allows us to identify important nodes in the network. We apply the proposed method to analyze the structure of several heterogeneous networks. We show that exploiting additional sources of evidence corresponding to links between, as well as among, different entity types yields new insights into network structure

On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation

Graph embedding has been proven to be efficient and effective in facilitating graph analysis. In this paper, we present a novel spectral framework called NOn-Backtracking Embedding (NOBE), which offers a new perspective that organizes graph data at a deep level by tracking the flow traversing on the edges with backtracking prohibited. Further, by analyzing the non-backtracking process, a technique called graph approximation is devised, which provides a channel to transform the spectral decomposition on an edge-to-edge matrix to that on a node-to-node matrix. Theoretical guarantees are provided by bounding the difference between the corresponding eigenvalues of the original graph and its graph approximation. Extensive experiments conducted on various real-world networks demonstrate the efficacy of our methods on both macroscopic and microscopic levels, including clustering and structural hole spanner detection.Comment: SDM 2018 (Full version including all proofs

Codifying and commodifying nature: Narratives on forest property rights and the implementation of tenure regularization policies in Northwestern Argentina

Environmental resource management requires negotiation among state and non-state actors with conflicting goals and different levels of influence. In northwestern Argentina, forest policy implementation is described as weak, due to governance structure and ambiguities in the law. We studied how policy actors’ attitudes and their positions in the forest governance network relate to the implementation of land tenure regularization in a context where land tenure regularization is at the core of struggles over environmental policies. We focused on the Chaco Salteño part of the Gran Chaco ecosystem, one of the world’s major deforestation frontiers. We argue that the presence of weak advocacy coalitions requires an analysis of agency to understand this policy process. Our policy network analysis revealed a lack of clear contrasting factions, due to a core–periphery structure. The core of the network brings together all core beliefs but not all of the most influential actors. Assessing network centrality and reputational influence enabled us to identify actors with exceptional agency. We contribute to the debates on advocacy coalitions and on land tenure by distinguishing between attitudes toward tenure regularization policies and their actual implementation in a context where actors have diverging interests and objectives