Supervised Random Walks: Predicting and Recommending Links in Social Networks

Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction

Graph cluster randomization: network exposure to multiple universes

A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the overall population to it. A drawback with A/B testing is that it is poorly suited for experiments involving social interference, when the treatment of individuals spills over to neighboring individuals along an underlying social network. In this work, we propose a novel methodology using graph clustering to analyze average treatment effects under social interference. To begin, we characterize graph-theoretic conditions under which individuals can be considered to be `network exposed' to an experiment. We then show how graph cluster randomization admits an efficient exact algorithm to compute the probabilities for each vertex being network exposed under several of these exposure conditions. Using these probabilities as inverse weights, a Horvitz-Thompson estimator can then provide an effect estimate that is unbiased, provided that the exposure model has been properly specified. Given an estimator that is unbiased, we focus on minimizing the variance. First, we develop simple sufficient conditions for the variance of the estimator to be asymptotically small in n, the size of the graph. However, for general randomization schemes, this variance can be lower bounded by an exponential function of the degrees of a graph. In contrast, we show that if a graph satisfies a restricted-growth condition on the growth rate of neighborhoods, then there exists a natural clustering algorithm, based on vertex neighborhoods, for which the variance of the estimator can be upper bounded by a linear function of the degrees. Thus we show that proper cluster randomization can lead to exponentially lower estimator variance when experimentally measuring average treatment effects under interference.Comment: 9 pages, 2 figure

Electromagnetics from a quasistatic perspective

Quasistatics is introduced so that it fits smoothly into the standard textbook presentation of electrodynamics. The usual path from statics to general electrodynamics is rather short and surprisingly simple. A closer look reveals however that it is not without confusing issues as has been illustrated by many contributions to this Journal. Quasistatic theory is conceptually useful by providing an intermediate level in between statics and the full set of Maxwell's equations. Quasistatics is easier than general electrodynamics and in some ways more similar to statics. It is however, in terms of interesting physics and important applications, far richer than statics. Quasistatics is much used in electromagnetic modeling, an activity that today is possible on a PC and which also has great pedagogical potential. The use of electromagnetic simulations in teaching gives additional support for the importance of quasistatics. This activity may also motivate some change of focus in the presentation of basic electrodynamics

Comparative Raman Studies of Sr2RuO4, Sr3Ru2O7 and Sr4Ru3O10

The polarized Raman spectra of layered ruthenates of the Srn+1RunO3n+1 (n=1,2,3) Ruddlesden-Popper series were measured between 10 and 300 K. The phonon spectra of Sr3Ru2O7 and Sr4Ru3O10 confirmed earlier reports for correlated rotations of neighboring RuO6 octahedra within double or triple perovskite blocks. The observed Raman lines of Ag or B1g symmetry were assigned to particular atomic vibrations by considering the Raman modes in simplified structures with only one double or triple RuO6 layer per unit cell and by comparison to the predictions of lattice dynamical calculations for the real Pban and Pbam structures. Along with discrete phonon lines, a continuum scattering, presumably of electronic origin, is present in the zz, xx and xy, but not in the x'y' and zx spectra. Its interference with phonons results in Fano shape for some of the lines in the xx and xy spectra. The temperature dependencies of phonon parameters of Sr3Ru2O7 exhibit no anomaly between 10 and 300 K where no magnetic transition occurs. In contrast, two B1g lines in the spectra of Sr4Ru3O10, corresponding to oxygen vibrations modulating the Ru-O-Ru bond angle, show noticeable hardening with ferromagnetic ordering at 105 K, thus indicating strong spin-phonon interaction.Comment: 9 pages, 12 figure

Models and Algorithms for Graph Watermarking

We introduce models and algorithmic foundations for graph watermarking. Our frameworks include security definitions and proofs, as well as characterizations when graph watermarking is algorithmically feasible, in spite of the fact that the general problem is NP-complete by simple reductions from the subgraph isomorphism or graph edit distance problems. In the digital watermarking of many types of files, an implicit step in the recovery of a watermark is the mapping of individual pieces of data, such as image pixels or movie frames, from one object to another. In graphs, this step corresponds to approximately matching vertices of one graph to another based on graph invariants such as vertex degree. Our approach is based on characterizing the feasibility of graph watermarking in terms of keygen, marking, and identification functions defined over graph families with known distributions. We demonstrate the strength of this approach with exemplary watermarking schemes for two random graph models, the classic Erd\H{o}s-R\'{e}nyi model and a random power-law graph model, both of which are used to model real-world networks

Conceptualizing gratitude and appreciation as a unitary personality trait

Gratitude and appreciation are currently measured using three self-report instruments, the GQ6 (1 scale), the Appreciation Scale (8 scales), and the GRAT (3 scales). Two studies were conducted to test how these three instruments are interrelated, whether they exist under the same higher order factor or factors, and whether gratitude and appreciation is a single or multi-factorial construct. In Study 1 (N = 206) all 12 scales were subjected to an exploratory factor analysis. Both parallel analysis and the minimum average partial method indicated a clear one-factor solution. In Study 2 (N = 389) multigroup confirmatory factor analysis supported the one-factor structure, demonstrated the invariance of this structure across gender, and ruled out the confounding effect of socially desirable responding. We conclude gratitude and appreciation are a single-factor personality trait. We suggest integration of gratitude and appreciation literatures and provide a clearer conceptualization of gratitude

Flow graphs: interweaving dynamics and structure

The behavior of complex systems is determined not only by the topological organization of their interconnections but also by the dynamical processes taking place among their constituents. A faithful modeling of the dynamics is essential because different dynamical processes may be affected very differently by network topology. A full characterization of such systems thus requires a formalization that encompasses both aspects simultaneously, rather than relying only on the topological adjacency matrix. To achieve this, we introduce the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights. Flow graphs provide an integrated representation of the structure and dynamics of the system, which can then be analyzed with standard tools from network theory. Conversely, a structural network feature of our choice can also be used as the basis for the construction of a flow graph that will then encompass a dynamics biased by such a feature. We illustrate the ideas by focusing on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and also explore their dual consensus dynamics.Comment: 4 pages, 1 figur

Analytical reasoning task reveals limits of social learning in networks

Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias,' which limits their social learning to the output, rather than the process, of their peers' reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning

Risk of electrocution during fire suppression activities involving photovoltaic systems

Firefighting activities regarding buildings normally require electric power to be disconnected before a water jet is used, in order to minimize the risk of electrocution. As for as concerns Photovoltaic Systems, during a fire event it is not possible to turn off the whole power system in order to guarantee that all the components are de-energized. The object of this paper is to estimate the safe distances to respect during firefighting involving PV Systems. To this end a series of experimental tests have been performed, in order to measure the current flowing through the water stream, under different conditions of nozzle design, jet shape, water pressure and stream length. Experimental results have been compared with data in literature. Moreover, the electrical conductivity of the water streams, which actually consists of water mixed with air, has been evaluate