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

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

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

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

An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

Impact of information letters on the reporting rate of adverse drug reactions and the quality of the reports: a randomized controlled study

BACKGROUND: Spontaneous reporting of adverse drug reactions (ADRs) is an important method for pharmacovigilance, but under-reporting and poor quality of reports are major limitations. The aim of this study was to evaluate if repeated one-page ADR information letters affect (i) the reporting rate of ADRs and (ii) the quality of the ADR reports. METHODS: All 151 primary healthcare units in the Region Västra Götaland, Sweden, were randomly allocated (1:1) to an intervention (n = 77) or a control group (n = 74). The intervention consisted of one-page ADR information letters administered at three occasions during 2008 to all physicians and nurses in the intervention units. The number of ADR reports received from the 151 units was registered, as was the quality of the reports, which was defined as high if the ADR was to be reported according to Swedish regulations, that is, if the ADR was (i) serious, (ii) unexpected, and/or (iii) related to the use of new drugs and not labelled as common in the Summary of Product Characteristics. A questionnaire was administered to evaluate if the ADR information letter had reached the intended recipient. RESULTS: Before the intervention, no significant differences in reporting rate or number of high quality reports could be detected between the randomization groups. In 2008, 79 reports were sent from 37 intervention units and 52 reports from 30 control units (mean number of reports per unit ± standard deviation: 1.0 ± 2.5 vs. 0.7 ± 1.2, P = 0.34). The number of high quality reports was higher in intervention units than in control units (37 vs. 15 reports, 0.5 ± 0.9 vs. 0.2 ± 0.6, P = 0.048). According to the returned questionnaires (n = 1,292, response rate 57%), more persons in the intervention than in the control group had received (29% vs. 19%, P < 0.0001) and read (31% vs. 26%, P < 0.0001) an ADR information letter. CONCLUSIONS: This study suggests that repeated ADR information letters to physicians and nurses do not increase the ADR reporting rate, but may increase the number of high quality reports

Impact of information letters on the reporting rate of adverse drug reactions and the quality of the reports: a randomized controlled study

BACKGROUND: Spontaneous reporting of adverse drug reactions (ADRs) is an important method for pharmacovigilance, but under-reporting and poor quality of reports are major limitations. The aim of this study was to evaluate if repeated one-page ADR information letters affect (i) the reporting rate of ADRs and (ii) the quality of the ADR reports. METHODS: All 151 primary healthcare units in the Region Västra Götaland, Sweden, were randomly allocated (1:1) to an intervention (n = 77) or a control group (n = 74). The intervention consisted of one-page ADR information letters administered at three occasions during 2008 to all physicians and nurses in the intervention units. The number of ADR reports received from the 151 units was registered, as was the quality of the reports, which was defined as high if the ADR was to be reported according to Swedish regulations, that is, if the ADR was (i) serious, (ii) unexpected, and/or (iii) related to the use of new drugs and not labelled as common in the Summary of Product Characteristics. A questionnaire was administered to evaluate if the ADR information letter had reached the intended recipient. RESULTS: Before the intervention, no significant differences in reporting rate or number of high quality reports could be detected between the randomization groups. In 2008, 79 reports were sent from 37 intervention units and 52 reports from 30 control units (mean number of reports per unit ± standard deviation: 1.0 ± 2.5 vs. 0.7 ± 1.2, P = 0.34). The number of high quality reports was higher in intervention units than in control units (37 vs. 15 reports, 0.5 ± 0.9 vs. 0.2 ± 0.6, P = 0.048). According to the returned questionnaires (n = 1,292, response rate 57%), more persons in the intervention than in the control group had received (29% vs. 19%, P < 0.0001) and read (31% vs. 26%, P < 0.0001) an ADR information letter. CONCLUSIONS: This study suggests that repeated ADR information letters to physicians and nurses do not increase the ADR reporting rate, but may increase the number of high quality reports

Active Re-identification Attacks on Periodically Released Dynamic Social Graphs

Active re-identification attacks pose a serious threat to privacy-preserving social graph publication. Active attackers create fake accounts to build structural patterns in social graphs which can be used to re-identify legitimate users on published anonymised graphs, even without additional background knowledge. So far, this type of attacks has only been studied in the scenario where the inherently dynamic social graph is published once. In this paper, we present the first active re-identification attack in the more realistic scenario where a dynamic social graph is periodically published. The new attack leverages tempo-structural patterns for strengthening the adversary. Through a comprehensive set of experiments on real-life and synthetic dynamic social graphs, we show that our new attack substantially outperforms the most effective static active attack in the literature by increasing the success probability of re-identification by more than two times and efficiency by almost 10 times. Moreover, unlike the static attack, our new attack is able to remain at the same level of effectiveness and efficiency as the publication process advances. We conduct a study on the factors that may thwart our new attack, which can help design graph anonymising methods with a better balance between privacy and utility