Tad Hogg

Using a model of social dynamics to predict popularity of news

Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity a priori, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.

Effects of feedback and peer pressure on contributions to enterprise social media

Increasingly, large organizations are experimenting with internal social media (e.g., blogs, forums) as a platform for widespread distributed collaboration. Contributions to their counterparts outside the organizations firewall are driven by attention from strangers, in addition to sharing among friends. However, employees in a workplace under time pressures may be reluctant to participate and the audience for their contributions is comparatively smaller. Participation rates also vary widely from group to group. So what influences people to contribute in this environment?n In this paper, we present the results of a year-long empirical study of internal social media participation at a large technology company, and analyze the impact attention, feedback, and managers and coworkers participation have on employees behavior. We find feedback in the form of posted comments is highly correlated with a users subsequent participation. Recent manager and coworker activity relate to users initiating or resuming participation in social media. These findings extend, to an aggregate level, the results from prior interviews about blogging at the company and offer design and policy implications for organizations seeking to encourage social media adoption.

Nanorobot architecture for medical target identification

This work has an innovative approach for the development of nanorobots with sensors for medicine. The nanorobots operate in a virtual environment comparing random, thermal and chemical control techniques. The nanorobot architecture model has nanobioelectronics as the basis for manufacturing integrated system devices with embedded nanobiosensors and actuators, which facilitates its application for medical target identification and drug delivery. The nanorobot interaction with the described workspace shows how time actuation is improved based on sensor capabilities. Therefore, our work addresses the control and the architecture design for developing practical molecular machines. Advances in nanotechnology are enabling manufacturing nanosensors and actuators through nanobioelectronics and biologically inspired devices. Analysis of integrated system modeling is one important aspect for supporting nanotechnology in the fast development towards one of the most challenging new fields of science: molecular machines. The use of 3D simulation can provide interactive tools for addressing nanorobot choices on sensing, hardware architecture design, manufacturing approaches, and control methodology investigation. (Some figures in this article are in colour only in the electronic version)

Friends and foes: ideological social networking

Traditional online social network sites use a single monolithic friends relationship to link users. However, users may have more in common with strangers, suggesting the use of a similarity network to recommend content. This paper examines the usefulness of this distinction in propagating new content. Using both macroscopic and microscopic social dynamics, we present an analysis of Essembly, an ideological social network that semantically distinguishes between friends and ideological allies and nemeses. Although users have greater similarity with their allies than their friends and nemeses, surprisingly, the allies network does not affect voting behavior, despite being as large as the friends network. In contrast, users are influenced differently by their friends and nemeses, indicating that people use these networks for distinct purposes. We suggest resulting design implications for social content aggregation services and recommender systems.

Enhancing reputation mechanisms via online social networks

Economic transactions often rely on trust. For instance, sellers usually know more than buyers about items or services offered and could misrepresent them. Without trust, mutually beneficial transactions may not take place, resulting in economic loss [4]. Fortunately, with repeated transactions, reputations [9] can encourage truthful behavior and have been applied to e-commerce in the form of ratings, such as those used by eBay. However, reputation mechanisms can be manipulated through collusion by groups of friends or the creation of false identities. One approach to this problem uses the growing availability of online social networks. These consist of links among individuals indicating various social relationships. Services building online social networks, such as Friendster, LinkedIn, and Spoke, have rapidly acquired millions of users and assist them in forming new social or business contacts through those they already have. These existing contacts are either entered manually or gathered automatically, e.g., from email, instant messaging, and the web of trust for decentralized cryptographic keys [6]. We consider two uses of social networks for reputation mechanisms: as an automated aid to identify reputation based on position in the network, and as a filtering tool for users’ ratings. The thresholds and weights we describe can be efficiently computed using the network structure. The first approach uses an individual’s position in a social network to compute an implicit reputation [14, 12, 3], without requiring explicit effort on the part of users to rate one another. This approach is useful to the extent that social connectivity correlates with likely behavior. One way to assign reputation based on social network structure considers each link in the network as an implicit

Experimental implementation of an adiabatic quantum optimization algorithm.

We report the realization of a nuclear magnetic resonance computer with three quantum bits that simulates an adiabatic quantum optimization algorithm. Adiabatic quantum algorithms offer new insight into how quantum resources can be used to solve hard problems. This experiment uses a particularly well-suited three quantum bit molecule and was made possible by introducing a technique that encodes general instances of the given optimization problem into an easily applicable Hamiltonian. Our results indicate an optimal run time of the adiabatic algorithm that agrees well with the prediction of a simple decoherence model.

Defect-tolerant Logic with Nanoscale Crossbar Circuits

Crossbar architectures are one approach to molecular electronic circuits for memory and logic applications. However, currently feasible manufacturing technologies for molecular electronics introduce numerous defects so insisting on defect-free crossbars would give unacceptably low yields. Instead, increasing the area of the crossbar provides enough redundancy to implement circuits in spite of the defects. We identify reliability thresholds in the ability of defective crossbars to implement boolean logic. These thresholds vary among different implementations of the same logical formula, allowing molecular circuit designers to trade-off reliability, circuit area, crossbar geometry and the computational complexity of locating functional components. We illustrate these choices for binary adders. For instance, one adder implementation yields functioning circuits 90% of the time with 30% defective crossbar junctions using an area only 1.8 times larger than the minimum required for a defect-free crossbar. We also describe an algorithm for locating a combination of functional junctions that can implement an adder circuit in a defective crossbar.

Adiabatic quantum computing for random satisfiability problems

The discrete formulation of adiabatic quantum computing is compared with other search methods, classical and quantum, for random satisfiability (SAT) problems. With the number of steps growing only as the cube of the number of variables, the adiabatic method gives solution probabilities close to 1 for problem sizes feasible to evaluate via simulation on current computers. However, for these sizes the minimum energy gaps of most instances are fairly large, so the good performance scaling seen for small problems may not reflect asymptotic behavior where costs are dominated by tiny gaps. Moreover, the resulting search costs are much higher than for other methods. Variants of the quantum algorithm that do not match the adiabatic limit give lower costs, on average, and slower growth than the conventional GSAT heuristic method.

Inferring preference correlations from social networks

Abstract Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer’s network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products.

Acoustic communication for medical nanorobots

Abstract Communication among microscopic robots (nanorobots) can coordinate their activities for biomedical tasks. The feasibility of in vivo xa0ultrasonic communication is evaluated for micron-size robots broadcasting into various types of tissues. Frequencies between 10 MHz and 300 MHz give the best tradeoff between efficient acoustic generation and attenuation for communication over distances of about 100 microns. Based on these results, we find power available from ambient oxygen and glucose in the bloodstream can readily support communication rates of about 10 4 bits/s between micron-sized robots. We discuss techniques, such as directional acoustic beams, that can increase this rate. The acoustic pressure fields enabling this communication are unlikely to damage nearby tissue, and short bursts at considerably higher power could be of therapeutic use.