28,561 research outputs found
Maximizing the Diversity of Exposure in a Social Network
Social-media platforms have created new ways for citizens to stay informed
and participate in public debates. However, to enable a healthy environment for
information sharing, social deliberation, and opinion formation, citizens need
to be exposed to sufficiently diverse viewpoints that challenge their
assumptions, instead of being trapped inside filter bubbles. In this paper, we
take a step in this direction and propose a novel approach to maximize the
diversity of exposure in a social network. We formulate the problem in the
context of information propagation, as a task of recommending a small number of
news articles to selected users. We propose a realistic setting where we take
into account content and user leanings, and the probability of further sharing
an article. This setting allows us to capture the balance between maximizing
the spread of information and ensuring the exposure of users to diverse
viewpoints.
The resulting problem can be cast as maximizing a monotone and submodular
function subject to a matroid constraint on the allocation of articles to
users. It is a challenging generalization of the influence maximization
problem. Yet, we are able to devise scalable approximation algorithms by
introducing a novel extension to the notion of random reverse-reachable sets.
We experimentally demonstrate the efficiency and scalability of our algorithm
on several real-world datasets
Opinion dynamics with varying susceptibility to persuasion
A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates
Centrality Metric for Dynamic Networks
Centrality is an important notion in network analysis and is used to measure
the degree to which network structure contributes to the importance of a node
in a network. While many different centrality measures exist, most of them
apply to static networks. Most networks, on the other hand, are dynamic in
nature, evolving over time through the addition or deletion of nodes and edges.
A popular approach to analyzing such networks represents them by a static
network that aggregates all edges observed over some time period. This
approach, however, under or overestimates centrality of some nodes. We address
this problem by introducing a novel centrality metric for dynamic network
analysis. This metric exploits an intuition that in order for one node in a
dynamic network to influence another over some period of time, there must exist
a path that connects the source and destination nodes through intermediaries at
different times. We demonstrate on an example network that the proposed metric
leads to a very different ranking than analysis of an equivalent static
network. We use dynamic centrality to study a dynamic citations network and
contrast results to those reached by static network analysis.Comment: in KDD workshop on Mining and Learning in Graphs (MLG
Disrupt, Deny, Dismantle: A Special Operations Forces (SOF) Model for Combatting New Terrorism
Terrorism in the new millennium has morphed drastically since the 1970s. The terrorist organizations of today are a hybrid between the insurgent group models of the 1960s and modern terrorist organizations such as Al Qaeda. This hybrid model has created what has become a transnational insurgency recruited, trained, and led by major terrorist networks such as the Islamic State of Iraq and the Levant (ISIL). Even smaller regional groups such as Boko Haram have surpassed merely conducting terrorist attacks. These smaller groups are also focused on controlling territory. Tan (2008) refers to this change as âNew Terrorismâ. To combat New Terrorism, a combination of counterinsurgency tactics and counterterrorism tactics must be employed. This study will examine the need to define roles and responsibilities for various organization and various echelons through the introduction of a new Special Operations Forces model; Disrupt, Deny, Dismantle. The acronym to be used for this model is D3. This model recommends different tactics, techniques, and procedures for forces not specifically assigned the counterterrorism mission. As new terrorism continues to change, only counterterrorism forces should be tasked with the Find Fix Finish, Exploit, Analyze, and Disseminate (F3EAD) model of targeting (Counterterrorism 2014). All other military and law enforcement elements should disrupt and deny the enemy in support of the counterterrorism effort. This study is based on extensive research and the authorâs 23 years of experience serving in U.S. Army Special Forces. Throughout his career, the author interacted with people from various social, economic, and professional backgrounds throughout the Middle East, Africa, and the Balkans
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
Productivity Effects of Information Diffusion in Networks
We examine the drivers of diffusion of information through organizations and the effects on performance. In particular, we ask: What predicts the likelihood of an individual becoming aware of a strategic piece of information, or becoming aware of it sooner? Do different types of information exhibit different diffusion patterns, and do different characteristics of social structure, relationships and individuals in turn affect access to different kinds of information? Does better access to information predict an individualâs ability to complete projects or generate revenue? We hypothesize that the dual effects of content and structure jointly predict the diffusion path of information, and ultimately performance. To test our hypotheses, we characterize the social network of a medium sized executive recruiting firm using accounting data on project co-work relationships and ten months of email traffic observed over two five month periods. We identify two distinct types of information diffusing over this network â âevent newsâ and âdiscussion topicsâ â by their usage characteristics, and observe several thousand diffusion processes of each type of information from their original first use to their varied recipients over time. We then test the effects of network structure and functional and demographic characteristics of dyadic relationships on the likelihood of receiving each type of information and receiving it more quickly. Our results demonstrate that the diffusion of news, characterized by a spike in communication and rapid, pervasive diffusion through the organization, is influenced by demographic and network factors but not by functional relationships (e.g. prior co-work, authority) or the strength of ties. In contrast, diffusion of discussion topics, which exhibit more shallow diffusion characterized by âback-and-forthâ conversation, is heavily influenced by functional relationships and the strength of ties, as well as demographic and network factors. Discussion topics are more likely to diffuse vertically up and down the organizational hierarchy, across relationships with a prior working history, and across stronger ties, while news is more likely to diffuse laterally as well as vertically, and without regard to the strength or function of relationships. Furthermore, we find that access to information strongly predicts the number of projects completed by each individual and the amount of revenue that person generates. The effects are economically significant, with each additional âword seenâ correlated with about $70 of additional revenue generated. Our findings highlight the importance of simultaneous considerations of structure and content in information diffusion studies and provide some of the first evidence on the economic importance of information diffusion in networks.The National Science Foundation, Cisco Systems, France Telecom and the MIT Center for Digital Busines
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