Networks are natural analytic tools in modeling adversarial activities(e.g., human trafficking, illicit drug production, terrorist financial transaction) using different intelligence data sources. However, such activities are often covert and embedded across multiple domains and contexts. They are generally not detectable and recognizable from the perspective of an isolated network, and only become apparent when multiple networks are analyzed in a joint manner. Thus, one of the main research topics in modeling adversarial activities is to develop effective techniques to align and fuse information from different networks into a unified representation for global analysis. Based on the combined network representation, an equally important research topic is on detecting and matching indicating patterns to recognize the underlining adversarial activities in the integrated network. The focus of this workshop is to gather together the researchers from all relevant fields to share their experience and opinions on graph mining techniques in the era of big data, with emphasis on two fundamental problems - "Connecting the dots" and "finding a needle in a haystack", in the context of graph-based adversarial activity analytics.

Sentiment analysis has been studied for decades, and it is widely used in many real applications such as media monitoring. In sentiment analysis, when addressing the problem of limited labeled data from the target domain, transfer learning, or domain adaptation, has been successfully applied, which borrows information from a relevant source domain with abundant labeled data to improve the prediction performance in the target domain. The key to transfer learning is how to model the relatedness among different domains. For sentiment analysis, a common practice is to assume similar sentiment polarity for the common keywords shared by different domains. However, existing methods largely overlooked the human factor, i.e., the users who expressed such sentiment. In this paper, we address this problem by explicitly modeling the human factor related to sentiment classification. In particular, we assume that the content generated by the same user across different domains is biased in the same way in terms of the sentiment polarity. In other words, optimistic/pessimistic users demonstrate consistent sentiment patterns, no matter what the context is. To this end, we propose a new graph-based approach named U-Cross, which models the relatedness of different domains via both the shared users and keywords. It is non-parametric and semi-supervised in nature. Furthermore, we also study the problem of shared user selection to prevent ‘negative transfer’. In the experiments, we demonstrate the effectiveness of U-Cross by comparing it with existing state-of-the-art techniques on three real data sets.

Networks are prevalent in many high impact domains. Moreover, cross-domain interactions are frequently observed in many applications, which naturally form the dependencies between different networks. Such kind of highly coupled network systems are referred to as multi-layered networks, and have been used to characterize various complex systems, including critical infrastructure networks, cyber-physical systems, collaboration platforms, biological systems, and many more. Different from single-layered networks where the functionality of their nodes is mainly affected by within-layer connections, multi-layered networks are more vulnerable to disturbance as the impact can be amplified through cross-layer dependencies, leading to the cascade failure to the entire system. To manipulate the connectivity in multi-layered networks, some recent methods have been proposed based on two-layered networks with specific types of connectivity measures. In this paper, we address the above challenges in multiple dimensions. First, we propose a family of connectivity measures (SUBLINE) that unifies a wide range of classic network connectivity measures. Third, we reveal that the connectivity measures in the SUBLINE family enjoy diminishing returns property, which guarantees a near-optimal solution with linear complexity for the connectivity optimization problem. Finally, we evaluate our proposed algorithm on real data sets to demonstrate its effectiveness and efficiency.

Networks appear naturally in many high-impact real-world applications. In an increasingly connected and coupled world, the networks arising from many application domains are often collected from different channels, forming the so-called multi-layered networks, such as cyber-physical systems, organization-level collaboration platforms, critical infrastructure networks and many more. Compared with single-layered networks, multi-layered networks are more vulnerable as even a small disturbance on one supporting layer/network might cause a ripple effect to all the dependent layers, leading to a catastrophic/cascading failure of the entire system. The state-of-the-art has been largely focusing on modeling and manipulating the cascading effect of two-layered interdependent network systems for some specific type of network connectivity measure. This paper generalizes the challenge to multiple dimensions. First, we propose a new data model for multi-layered networks MULAN, which admits an arbitrary number of layers with a much more flexible dependency structure among different layers, beyond the current pair-wise dependency. Second, we unify a wide range of classic network connectivity measures SUBLINE. Third, we show that for any connectivity measure in the SUBLINE family, it enjoys the diminishing returns property which in turn lends itself to a family of provable near-optimal control algorithms with linear complexity. Finally, we conduct extensive empirical evaluations on real network data, to validate the effectiveness of the proposed algorithms.

Communication in multi- and many-core processors has long been a bottleneck to performance due to the high cost of long-distance electrical transmission. This difficulty has been partially remedied by architectural constructs such as caches and novel interconnect topologies, albeit at a steep cost in terms of complexity. Unfortunately, even these measures are rendered ineffective by certain kinds of communication, most notably scatter and gather operations that exhibit highly nonlocal data access patterns. Much work has gone into examining how the increased bandwidth density afforded by chip-scale silicon photonic interconnect technologies affects computing, but photonics have additional properties that can be leveraged to greatly accelerate performance and energy efficiency under such difficult loads. This paper describes a novel synchronized global photonic bus and system architecture called P-sync that uses photonics' distance independence to greatly improve performance on many important applications previously limited by electronic interconnect. The architecture is evaluated in the context of a non-local yet common application: the distributed Fast Fourier Transform. We show that it is possible to achieve high efficiency by tightly balancing computation and communication latency in P-sync and achieve upwards of a 6× performance increase on gather patterns, even when bandwidth is equalized.

When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that an approximation to the exact model yields an exploitable structure in the edge probabilities, allowing for efficient scaling of a spectral framework for anomaly detection through analysis of graph residuals, and a fast and simple procedure for estimating the model parameters. In simulation, we demonstrate that taking into account both attributes and dynamics in this analysis has a much more significant impact on the detection of an emerging anomaly than accounting for either dynamics or attributes alone. We also present an analysis of a large, dynamic citation graph, demonstrating that taking additional document metadata into account emphasizes parts of the graph that would not be considered significant otherwise.

As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non-Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One model currently in use is the Chung-Lu random graph model, in which edge probabilities are expressed in terms of a given expected degree sequence. An advantage of this model is that its parameters can be obtained via a simple, standard estimator. Although this estimator is used frequently, its statistical properties have not been fully studied. In this paper, we develop a central limit theory for a simplified version of the Chung-Lu parameter estimator. We then derive approximations for moments of the general estimator using the delta method, and confirm the effectiveness of these approximations through empirical examples.

A crucial element of large web companies is their ability to collect and analyze massive amounts of data. Tuple store databases are a key enabling technology employed by many of these companies (e.g., Google Big Table and Amazon Dynamo). Tuple stores are highly scalable and run on commodity clusters, but lack interfaces to support efficient development of mathematically based analytics. D4M (Dynamic Distributed Dimensional Data Model) has been developed to provide a mathematically rich interface to tuple stores (and structured query language “SQL” databases). D4M allows linear algebra to be readily applied to databases. Using D4M, it is possible to create composable analytics with significantly less effort than using traditional approaches. This work describes the D4M technology and its application and performance.

In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filtering, and signal detection. We describe the architecture, which covers the entire processing chain, from data storage to graph construction to graph analysis and subgraph detection. The data are stored in a new format that allows easy extraction of graphs representing any relationship existing in the data. The principal analysis algorithm is the partial eigendecomposition of the modularity matrix, whose running time is discussed. A large document dataset is analyzed, and we present subgraphs that stand out in the principal eigenspace of the time-varying graphs, including behavior we regard as clutter as well as small, tightly-connected clusters that emerge over time.

Anomaly detection in graphs is a relevant problem in numerous applications. When determining whether an observation is anomalous with respect to the model of typical behavior, the notion of “goodness of fit” is important. This notion, however, is not well-understood in the context of graph data. In this paper, we propose three goodness-of-fit statistics for Chung-Lu random graphs, and analyze their efficacy in discriminating graphs generated by the Chung-Lu model from those with anomalous topologies. In the results of a Monte Carlo simulation, we see that the most powerful statistic for anomaly detection depends on the type of anomaly, suggesting that a hybrid statistic would be the most powerful.

Most video exploitation algorithms operate on individual frames. To effect good results in such applications, the algorithms require good frames with which to work. However, videos may contain artifacts such as blur which inhibit the extraction of inherent and useful information. This paper proposes an algorithm that detects poor video frames induced by global motion blur. The proposed algorithm is divided into two steps: the first of which creates a single image blur metric, and the second of which adds temporal information. The blur metric is derived from a linear least squares fit to the log distribution of the highest subbands in a wavelet-based Haar filters. The second part of the algorithm correlates adjacent frames to boost performance. The ideas presented in this paper are low in complexity yet high in performance. Additionally, the proposed algorithm has been tested on natural video data as well as synthesized blur, and comparisons to state of the art show an advantage in using wavelet-based thresholding.

This letter proposes a new model for preferential attachment in dynamic directed networks. This model consists of a linear time-invariant system that uses past observations to predict future attachment rates, and an innovation noise process that induces growth on vertices that previously had no attachments. Analyzing a large citation network in this context, we show that the proposed model fits the data better than existing preferential attachment models. An analysis of the noise in the dataset reveals power-law degree distributions often seen in large networks, and polynomial decay with respect to age in the probability of citing yet-uncited documents.

Image geo-registration is the process of relating a photograph and its pose to referenced world coordinates. The application is relevant, especially to the social networking, photo-sharing, and intelligence communities, where the locations of objects of interest are to be determined. This paper proposes an algorithm that identifies and geo-registers query photographs in the absence of any meta-data that are spatially co-located with training sets of user-collected images. Training images construct a 3D model using inherent structure from motion between images. Using SIFT features, the 3D model fuses co-located points and generates an averaged SIFT comparison feature. This paper also advises on training set augmentation to mitigate deleterious illumination, seasonal, and weather effects by introducing methods of merging separate point clouds. As such, probability of detection (and therefore, registration performance) is enhanced. After the 3D model is generated, each test photograph is matched to the 3D point cloud and registered using similar camera refinement and positional optimization techniques. The results provide accuracy to within a few meters of the absolute geo-coordinates.

Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example: detection of a subgraph embedded in a background graph. We describe an approach based on detection theory and provide empirical results indicating that the test statistic proposed has reasonable power to detect dense subgraphs in large random graphs.

The proposed algorithm identifies whether or not a test photo belongs to a set of co-located training images based on its spatial proximity to the training set. We leverage concepts from Lowe's SIFT and Snavely's Photo Tourism algorithms, and match an image by its 2D features to the 3D features representing the training set. To reduce complexity and increase efficiency, the proposed algorithm implements a compact representation of the image set by merging collections similar features. Test images are then matched with the derived structure. Finally, a decision statistic is determined based on the percentage of features that match. Receiver operating characteristics, computational analysis, and distributions are included in the performance analysis.

When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies. Nevertheless, it is desirable to determine the detectability of small, anomalous graphs embedded into background networks with known statistical properties. Casting the problem of subgraph detection in a signal processing context, this article provides a framework and empirical results that elucidate a detection theory" for graph-valued data. Its focus is the detection of anomalies in unweighted, undirected graphs through L1 properties of the eigenvectors of the graph’s so-called modularity matrix. This metric is observed to have relatively low variance for certain categories of randomly-generated graphs, and to reveal the presence of an anomalous subgraph with reasonable reliability when the anomaly is not well-correlated with stronger portions of the background graph. An analysis of subgraphs in real network datasets confirms the efficacy of this approach."

As advancements in CMOS technology trend toward ever increasing core counts in chip multiprocessors for high-performance embedded computing, the discrepancy between on- and off-chip communication bandwidth continues to widen due to the power and spatial constraints of electronic off-chip signaling. Silicon photonics-based communication offers many advantages over electronics for network-on-chip design, namely power consumption that is effectively agnostic to distance traveled at the chip- and board-scale, even across chip boundaries. In this work we develop a design for a photonic network-on-chip with integrated DRAM I/O interfaces and compare its performance to similar electronic solutions using a detailed network-on-chip simulation. When used in a circuit-switched network, silicon nanophotonic switches offer higher bandwidth density and low power transmission, adding up to over 10x better performance and 3-5x lower power over the baseline for projective transform, matrix multiply, and Fast Fourier Transform (FFT), all key algorithms in embedded real-time signal and image processing.

MATLAB® is one of the most commonly used languages for scientific computing with approximately one million users worldwide. At MIT Lincoln Laboratory, MATLAB is used by technical staff to develop sensor processing algorithms. MATLAB's popularity is based on availability of high-level abstractions leading to reduced code development time. Due to the compute intensive nature of scientific computing, these applications often require long running times and would benefit greatly from increased performance offered by parallel computing. pMatlab (www.ll.mit.edu/pMatlab) implements partitioned global address space (PGAS) support via standard operator overloading techniques. The core data structures in pMatlab are distributed arrays and maps, which simplify parallel programming by removing the need for explicit message passing. This paper presents the pMatlab design and results for the HPC Challenge benchmark suite. Additionally, two case studies of pMatlab use are described.