Ajitesh Srivastava

Influence in social networks: a unified model?

Understanding how information flows in online social networks is of great importance. It is generally difficult to obtain accurate prediction results of cascades over such networks, therefore a variety of diffusion models have been proposed in the literature to simulate diffusion processes instead. We argue that such models require extensive simulation results to produce good estimates of future spreads. In this work, we take a complimentary approach. We present a generalized, analytical model of influence in social networks that captures social influence at various levels of granularity, ranging from pairwise influence, to local neighborhood, to the general population, and external events, therefore capturing the complex dynamics of human behavior. We demonstrate that our model can integrate a variety of diffusion models. Particularly, we show that commonly used diffusion models in social networks can be reduced to special cases of our model, by carefully defining their parameters. Our goal is to provide a closed-form expression to approximate the probability of infection for every node in an arbitrary, directed network at any time t. We quantitatively evaluate the approximation quality of our analytical solution as compared to numerous popular diffusion models on a real-world dataset and a series of synthetic graphs.

A hybrid design for high performance large-scale sorting on FPGA

Sorting is a key kernel in numerous big data application including database operations, graphs and text analytics. Due to low control overhead, parallel bitonic sorting networks are usually employed for hardware implementations to accelerate sorting. Although a typical implementation of merge sort network can lead to low latency and small memory usage, it suffers from low throughput due to the lack of parallelism in the final stage. We analyze a pipelined merge sort network, showing its theoretical limits in terms of latency, memory and, throughput. To increase the throughput, we propose a merge sort based hybrid design where the final few stages in the merge sort network are replaced with “folded” bitonic merge networks. In these “folded” networks, all the interconnection patterns are realized by streaming permutation networks (SPN). We present a theoretical analysis to quantify latency, memory and throughput of our proposed design. Performance evaluations are performed by experiments on Xilinx Virtex-7 FPGA with post place-androute results. We demonstrate that our implementation achieves a throughput close to 10 GBps, outperforming state-of-the-art implementation of sorting on the same hardware by 1.2x, while preserving lower latency and higher memory efficiency.

Computational models of technology adoption at the workplace

Popular social networking sites have revolutionized the way people interact on the Web, enabling rapid information dissemination and search. In an enterprise, understanding how information flows within and between organizational levels and business units is of great importance. Despite numerous studies in information diffusion in online social networks, little is known about factors that affect the dynamics of technological adoption at the workplace. Here, we address this problem, by examining the impact of organizational hierarchy in adopting new technologies in the enterprise. Our study suggests that middle-level managers are more successful in influencing employees into adopting a new microblogging service. Further, we reveal two distinct patterns of peer pressure, based on which employees are not only more likely to adopt the service, but the rate at which they do so quickens as the popularity of the new technology increases. We integrate our findings into two intuitive, realistic agent-based computational models that capture the dynamics of adoption at both microscopic and macroscopic levels. We evaluate our models in a real-world dataset we collected from a multinational Fortune 500 company. Prediction results show that our models provide great improvements over commonly used diffusion models. Our findings provide significant insights to managers seeking to realize the dynamics of adoption of new technologies in their company and could assist in designing better strategies for rapid and efficient technology adoption and information dissemination at the workplace.

OSCAR: Optimizing SCrAtchpad reuse for graph processing

Recently, architectures with scratchpad memory are gaining popularity. These architectures consist of low bandwidth, large capacity DRAM and high bandwidth, user addressable small capacity scratchpad. Existing algorithms must be redesigned to take advantage of the high bandwidth while overcoming the constraint on capacity of scratchpad. In this paper, we propose an optimized edge-centric graph processing algorithm for scratchpad based architectures. Our key contribution is significant reduction in (slower) DRAM accesses through intelligent reuse of scratchpad data. We trade off reduction in DRAM accesses for slightly higher scratchpad accesses. However, due to the much higher bandwidth of scratchpad, the total memory access cost (DRAM + scratchpad) is significantly reduced. We validate our analysis with experiments on real world graphs using a simulator which mimics the scratchpad based architecture using Single Source Shortest Path (SSSP) and Breadth First Search (BFS). Our experimental results demonstrate 1.7× to 2.7× reduction in DRAM accesses leading to an improvement of 1.4× to 2× in total memory (DRAM + scratchpad) accesses.

The unified model of social influence and its application in influence maximization

The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. In an earlier work, we proposed a unified model which provides an approximate analytical solution to the problem of predicting probability of infection of every node in the network over time. Our model generalizes a large class of diffusion process. We demonstrate through extensive empirical evaluation that the error of approximation is small. We build upon our unified model to develop an efficient method for influence maximization. Unlike most approaches, we assume that diffusion spreads not only via the edges of the underlying network, but also through temporal functions of external out-of-network processes. We empirically evaluate our approach and compare it against state-of-the-art approaches on real-world large-scale networks. The evaluation demonstrates that our method has significant performance gains over widely used seed-set selection algorithms.

Mining Large Dense Subgraphs

Several applications including community detection in social networks and discovering correlated genes involve finding large subgraphs of high density. We propose the problem of finding the largest subgraph of a given density. The problem is a generalization of the Max-Clique problem which seeks the largest subgraph that has an edge density of 1. We define an objective function and prove that its optimization results in the largest graph of given density. We propose an algorithm that finds the subgraph by running multiple local search heuristics with random restarts. For massive graphs, where running the algorithm directly may be intractable, we use a sampling technique that reduces the graph to a smaller one which is likely to contain large dense subgraphs. We evaluate our algorithm on multiple real life and synthetic datasets. Our experiments show that our algorithm performs as well as the state-of-the-art for finding large subgraphs of high density, while providing density guarantees.

Computing competing cascades on signed networks

Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascade scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most “influential” nodes in the network. The majority of prior work, however, focuses on unsigned networks where individuals adopt the opinion of their neighbors with certain probability. In real life, relationships between individuals can be positive (e.g., friend of relationship) or negative (e.g., connection between “foes”). According to social theory, people tend to have similar opinions to their friends but opposite of their foes. We study the problem of competing cascades on signed networks, which has been relatively unexplored. Particularly, we study the progressive propagation of two competing cascades in a signed network under the Independent Cascade Model and Generalized Linear Threshold Model and provide an approximate analytical solution to compute the probability of infection of a node at any given time. We validate the quality of our approximation on several synthetic graphs. We leverage our analytical solution to the problem of competing cascades in signed networks to develop a heuristic for the influence maximization problem. We allow the seed-set to be initialized with populations of both cascades with the end goal of maximizing the spread of one cascade. We validate our approach on several large-scale real-world and synthetic networks. Our experiments demonstrate that our influence maximization heuristic significantly outperforms state-of-the-art methods, particularly when the network is dominated by distrust relationships.

Rapid Data Integration and Analysis for Upstream Oil and Gas Applications

The increasingly large number of sensors and instruments in the oil and gas industry, along with novel means of communication in the enterprise has led to a corresponding increase in the volume of data that is recorded in various information repositories. The variety of information sources is also expanding: from traditional relational databases to time series data, social network communications, collections of unsorted text reports, and linked data available on the Web. Enabling end-to-end optimization considering these diverse types of information requires creating semantic links between them. Though integration of data across silo-ed databases has been recognized as a problem for a long time, it has proven to be difficult to accomplish due to the complexity of the data arrangement within databases, scarcity of metadata that describe the content, lack of a direct mapping between related entities across databases, and the several types of data represented within a database. In addition, there are large amounts of unstructured text data such as text entries in databases and document repositories. These contain valuable information on processes from the field but there is currently no method to convert this raw data to useable information. The Center for Interactive Smart Oilfield Technologies (CiSoft) is a USC-Chevron Center of Excellence for Research and Academic Training on Smart Oilfield Technologies. We describe the Integrated Optimization project at CiSoft which has the goal of developing a framework for automated linking of heterogeneous data sources and analysis of the integrated data in the context of upstream applications.