Greg Ver Steeg

Information transfer in social media

Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information--theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine--grain notions of influence, and admits a natural, predictive interpretation. Networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real-world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups.

Entangling Power of an Expanding Universe

We show that entanglement can be used to detect spacetime curvature. Quantum fields in the Minkowski vacuum are entangled with respect to local field modes. This entanglement can be swapped to spatially separated quantum systems using standard local couplings. A single, inertial field detector in the exponentially expanding (de Sitter) vacuum responds as if it were bathed in thermal radiation in a Minkowski universe. We show that using two inertial detectors, interactions with the field in the thermal case will entangle certain detector pairs that would not become entangled in the corresponding de Sitter case. The two universes can thus be distinguished by their entangling power.

Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

We propose a temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space, and interactions are more likely to occur between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.We study temporal link prediction problem, where, given past interactions, our goal is to predict new interactions. We propose a dynamic link prediction method based on nonnegative matrix factorization. This method assumes that interactions are more likely between users that are similar to each other in the latent space representation. We propose a global optimization algorithm to effectively learn the temporal latent space with quadratic convergence rate and bounded error. In addition, we propose two alternative algorithms with local and incremental updates, which provide much better scalability without deteriorating prediction accuracy. We evaluate our model on a number of real-world dynamic networks and demonstrate that our model significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.

Modeling Temporal Activity Patterns in Dynamic Social Networks

The focus of this work is on developing probabilistic models for temporal activity of users in social networks (e.g., posting and tweeting) by incorporating the social network influence as perceived by the user. Although prior work in this area has developed sophisticated models for user activity, these models either ignore social network influence completely or incorporate it in an implicit manner. We overcome the nontransparency of the network in the model at the individual scale by proposing a coupled hidden Markov model (HMM), where each users activity evolves according to a Markov chain with a hidden state that is influenced by the collective activity of the friends of the user. We develop generalized Baum-Welch and Viterbi algorithms for parameter learning and state estimation for the proposed framework. We then validate the proposed model using a significant corpus of user activity on Twitter. Our numerical studies show that with sufficient observations to ensure accurate model learning, the proposed framework explains the observed data better than either a renewal process-based model or a conventional (uncoupled) HMM. We also demonstrate the utility of the proposed approach in predicting the time to the next tweet. Finally, clustering in the model parameter space is shown to result in distinct natural clusters of users characterized by the interaction dynamic between a user and his network.

Max 2-SAT with up to 108 qubits

We experimentally study the performance of a programmable quantum annealing processor, the D-Wave One (DW1) with up to 108 qubits, on maximum SAT problem with 2 variables per clause (MAX 2-SAT) problems. We consider ensembles of random problems characterized by a fixed clause density, an external parameter which we tune through its critical value in our experiments. We demonstrate that the DW1 is sensitive to the critical value of the clause density. The DW1 results are verified and compared with akmaxsat, an exact, state-of-the-art algorithm. We study the relative performance of the two solvers and how they correlate in terms of problem hardness. We find that the DW1 performance scales more favorably with problem size and that problem hardness correlation is essentially non-existent. We discuss the relevance and limitations of such a comparison.

Black holes are almost optimal quantum cloners

Black holes are quantum objects with intriguing characteristics. They are formed in the stellar collapse of stars with sufficient mass because such objects become relativistically unstable. While classically no signal can emerge from within the event horizon, Hawking showed that in curved-space quantum field theory, black holes must emit thermal radiation with a temperature T that is inversely proportional to the black hole’s mass M: T = 1/(8�M), in convenient units [2]. But Hawking’s calculation also created an apparent paradox. Because the eponymous radiation takes its energy from the mass of the black hole, the latter might ultimately evaporate. And if the emitted radiation is strictly thermal as the calculation suggests, the initial data about the formation of the black hole would have to be erased with the concomitant evaporation, something the laws of physics simply should not permit. The same appears to hold true for information directed at the event horizon after the formation of the black hole (this means “at late times” in the parlance of gravity, because according to stationary observers the black hole only forms for t → ∞). If the black hole final state does not depend on whether Shakespeare’s or Goethe’s works are absorbed by the black hole–given that both œuvres have the same mass–then space time dynamics would appear to be irreversible even if black holes never evaporate at all. Recently, we found that from the point of view of quantum communication theory, black holes are not so special after all [3]. Rather, they appear to be relatively ordinary noisy quantum communication channels, with a capacity to transmit classical information given by the expression proposed by Holevo [4], because any matter or radiation absorbed by the black hole must stimulate the emission of exact copies outside of the event horizon [5]. Because the absorbed particles are quantum states, we may wonder whether stimulated emission at the black hole horizon violates the quantum no-cloning theorem [6, 7]. It does not: the spontaneously emitted Hawking radiation prevents precisely that. But we may ask how well quantum states are copied by black holes, because this will shed light on how black holes treat quantum–rather than classical–signal states. We find that a black hole’s cloning fidelity depends on the probability with which quantum states are absorbed by the black hole, 0, with a cloning fidelity ranging from the classical measurement limit to the universal optimal cloner [8, 9].

Classical information transmission capacity of quantum black holes

The fate of classical information incident on a quantum black hole has been the subject of an ongoing controversy in theoretical physics, because a calculation within the framework of semi-classical curved-space quantum field theory appears to show that the incident information is irretrievably lost, in contradiction to time-honored principles such as time-reversibility and unitarity. Here, we show within this framework embedded in quantum communication theory that signaling from past to future null infinity in the presence of a Schwarzschild black hole can occur with arbitrary accuracy, and thus that classical information is not lost in black hole dynamics. The calculation relies on a treatment that is manifestly unitary from the outset, where probability conservation is guaranteed because black holes stimulate the emission of radiation in response to infalling matter. This stimulated radiation is non-thermal and contains all of the information about the infalling matter, while Hawking radiation contains none of it.

Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.

Disentangling the Lexicons of Disaster Response in Twitter

People around the world use social media platforms such as Twitter to express their opinion and share activities about various aspects of daily life. In the same way social media changes communication in daily life, it also is transforming the way individuals communicate during disasters and emergencies. Because emergency officials have come to rely on social media to communicate alerts and updates, they must learn how users communicate disaster related content on social media. We used a novel information-theoretic unsupervised learning tool, CorEx, to extract and characterize highly relevant content used by the public on Twitter during known emergencies, such as fires, explosions, and hurricanes. Using the resulting analysis, authorities may be able to score social media content and prioritize their attention toward those messages most likely to be related to the disaster.

Information-theoretic characterization of blood panel predictors for brain atrophy and cognitive decline in the elderly

Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimers Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations. The approach produces a hierarchical structure and the predictive power of biological variables and latent factors are compared with regression. We found that a group of variables containing the well-known AD risk gene APOE and CSF tau and amyloid levels were highly correlated. This latent factor was the most predictive of cognitive decline and brain atrophy.