Petko Bogdanov

Learning Predictive Substructures with Regularization for Network Data

Learning a succinct set of substructures that predicts global network properties plays a key role in understanding complex network data. Existing approaches address this problem by sampling the exponential space of all possible subnetworks to find ones of high prediction accuracy. In this paper, we develop a novel framework that avoids sampling by formulating the problem of predictive subnetwork learning as node selection, subject to network-constrained regularization. Our framework involves two steps: (i) subspace learning, and (ii) predictive substructures discovery with network regularization. The framework is developed based upon two mathematically sound techniques of spectral graph learning and gradient descent optimization, and we show that their solutions converge to a global optimum solution - a desired property that cannot be guaranteed by sampling approaches. Through experimental analysis on a number of real world datasets, we demonstrate the performance of our framework against state-of-the-art algorithms, not only based on prediction accuracy but also in terms of domain relevance of the discovered substructures.

Hierarchical in-network attribute compression via importance sampling

Many real-world complex systems can be modeled as dynamic networks with real-valued vertex/edge attributes. Examples include users opinions in social networks and average speeds in a road system. When managing these large dynamic networks, compressing attribute values becomes a key requirement, since it enables the answering of attribute-based queries regarding a node/edge or network region based on a compact representation of the data. To address this problem, we introduce a lossy network compression scheme called Slice Tree (ST), which partitions a network into smooth regions with respect to node/edge values and compresses each value as the average of its region. ST applies a compact representation for network partitions, called slices, that are defined as a center node and radius distance. We propose an importance sampling algorithm to efficiently prune the search space of candidate slices in the ST construction by biasing the sampling process towards the node values that most affect the compression error. The effectiveness of ST in terms of compression error, compression rate, and running time is demonstrated using synthetic and real datasets. ST scales to million-node instances and removes up to 87% of the error in attribute values with a 103 compression ratio. We also illustrate how ST captures relevant phenomena in real networks, such as research collaboration patterns and traffic congestions.

A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

Modeling and predicting peoples opinions plays an important role in todays life. For viral marketing and political strategy design, it is particularly important to be able to analyze competing opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network behaved abnormally? Furthermore, can we predict how the opinions of individual users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions spread beyond users ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously. In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen opinion dynamics model. SND has a rich semantics of a transportation problem, yet, is computable in pseudo-linear time, thereby, being applicable to large-scale social networks analysis. We demonstrate the effectiveness of SND in experiments with Twitter data.

Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. We investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or motifs, that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. This predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.

Adaptation of a visible wavelength fluorescence microplate reader for discovery of near-infrared fluorescent probes

We present an inexpensive, generalizable approach for modifying visible wavelength fluorescence microplate readers to detect emission in the near-infrared (NIR) I (650-950 nm) and NIR II (1000-1350 nm) tissue imaging windows. These wavelength ranges are promising for high sensitivity fluorescence-based cell assays and biological imaging, but the inaccessibility of NIR microplate readers is limiting development of the requisite, biocompatible fluorescent probes. Our modifications enable rapid screening of NIR candidate probes, using short pulses of UV light to provide excitation of diverse systems including dye molecules, semiconductor quantum dots, and metal clusters. To confirm the utility of our approach for rapid discovery of new NIR probes, we examine the silver cluster synthesis products formed on 375 candidate DNA strands that were originally designed to produce green-emitting, DNA-stabilized silver clusters. The fast, sensitive system developed here discovered DNA strands that unexpectedly stabilize NIR-emitting silver clusters.

Smallholder Agriculture in the Information Age: Limits and Opportunities

Recent projections by the United Nations show that the food production needs to double by 2050 in order to meet the nutrition demand of the worlds growing population. A key enabler of this growth are smallholder family farms, that form the backbone of agricultural (AG) production worldwide. To meet this increasing demand, smallholder farms need to implement critical advances in task management and coordination, crop and livestock monitoring and efficient farming practices. Information and Communication Technology (ICT) will play a critical role in these advances by providing integrated and affordable cyber-physical systems (CPS) that can longitudinally measure, analyze and control AG operations. In this paper we make headway towards the design and integration of such AG-CPS. We begin by characterizing the information and communication technology demand of smallholder agriculture based on traffic analysis of farm Internet use. Our findings inform the design and integration of an end-to-end AG-CPS called FarmNET that provides (i) robust control mechanisms for multi-sensor AG data collection and fusion, (ii) wide-area, heterogeneous wireless networks for ubiquitous farm connectivity, (iii) algorithms and models for farm data analytics that produce actionable information from the collected agricultural data, and (iv) control mechanisms for autonomous, proactive farming.

LARC: Learning Activity-Regularized Overlapping Communities Across Time

Communities are essential building blocks of complex networks enjoying significant research attention in terms of modeling and detection algorithms. Common across models is the premise that node pairs that share communities are likely to interact more strongly. Moreover, in the most general setting a node may be a member of multiple communities, and thus, interact with more than one cohesive group of other nodes. If node interactions are observed over a long period and aggregated into a single static network, the communities may be hard to discern due to their in-network overlap. Alternatively, if interactions are observed over short time periods, the communities may be only partially observable. How can we detect communities at an appropriate temporal resolution that resonates with their natural periods of activity? We propose LARC, a general framework for joint learning of the overlapping community structure and the periods of activity of communities, directly from temporal interaction data. We formulate the problem as an optimization task coupling community fit and smooth temporal activation over time. To the best of our knowledge, the tensor version of LARC is the first tensor-based community detection method to introduce such smoothness constraints. We propose efficient algorithms for the problem, achieving a

High throughput near infrared screening discovers DNA-templated silver clusters with peak fluorescence beyond 950 nm

2.6x

AirVIEW: Unsupervised transmitter detection for next generation spectrum sensing

quality improvement over all baselines for high temporal resolution datasets, and consistently detecting better-quality communities for different levels of data aggregation and varying community overlap. In addition, LARC elucidates interpretable temporal patterns of community activity corresponding to botnet attacks, transportation change points and public forum interaction trends, while being computationally practical---few minutes on large real datasets. Finally, LARC provides a comprehensive em unsupervised parameter estimation methodology yielding high accuracy and rendering it easy-to-use for practitioners.

Efficient spectrum summarization using compressed spectrum scans

We use high throughput near-infrared (NIR) screening technology to discover abundant new DNA-stabilized silver clusters, AgN-DNA, that fluoresce in the NIR. These include the longest wavelength AgN-DNA fluorophores identified to date, with peak emission beyond 950 nm that extends into the NIR II tissue transparency window, and the highest silver content.