Akhil Arora

Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study

Influence maximization (IM) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of IM techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in IM even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.

ASIM: A Scalable Algorithm for Influence Maximization under the Independent Cascade Model

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. Although, TIM is one of the fastest existing algorithms, it cannot be deemed scalable owing to its exorbitantly high memory footprint.cIn this paper, we address the scalability aspect -- memory consumption and running time of the influence maximization problem. We propose ASIM, a scalable algorithm capable of running within practical compute times on commodity hardware. Empirically, ASIM is

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

6-8

Mining statistically significant connected subgraphs in vertex labeled graphs

times faster when compared to CELF++ with similar memory consumption, while its memory footprint is

STAR: Real-time Spatio-Temporal Analysis and Prediction of Traffic Insights using Social Media

\approx 200

Efficient edit distance based string similarity search using deletion neighborhoods

times smaller when compared to TIM.

HD-index: pushing the scalability-accuracy boundary for approximate kNN search in high-dimensional spaces

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.

A Plant Identification System using Shape and Morphological Features on Segmented Leaflets: Team IITK, CLEF 2012.

The steady growth of graph data in various applications has resulted in wide-spread research in finding significant sub-structures in a graph. In this paper, we address the problem of finding statistically significant connected subgraphs where the nodes of the graph are labeled. The labels may be either discrete where they assume values from a pre-defined set, or continuous where they assume values from a real domain and can be multi-dimensional. We motivate the problem citing applications in spatial co-location rule mining and outlier detection. We use the chi-square statistic as a measure for quantifying the statistical significance. Since the number of connected subgraphs in a general graph is exponential, the naive algorithm is impractical. We introduce the notion of contracting edges that merge vertices together to form a super-graph. We show that if the graph is dense enough to start with, the number of super-vertices is quite low, and therefore, running the naive algorithm on the super-graph is feasible. If the graph is not dense, we provide an algorithm to reduce the number of super-vertices further, thereby providing a trade-off between accuracy and time. Empirically, the chi-square value obtained by this reduction is always within 96% of the optimal value, while the time spent is only a fraction of that for the optimal. In addition, we also show that our algorithm is scalable and it significantly enhances the ability to analyze real datasets.

METHODS AND SYSTEMS FOR IDENTIFYING TARGET USERS OF CONTENT

The steady growth of data from social networks has resulted in wide-spread research in a host of application areas including transportation, health-care, customer-care and many more. Owing to the ubiquity and popularity of transportation (more recently) the growth in the number of problems reported by the masses has no bounds. With the advent of social media, reporting problems has become easier than before. In this paper, we address the problem of efficient management of transportation related woes by leveraging the information provided by social media sources such as -- Facebook, Twitter etc. We develop techniques for viral event detection, identify frequently co-occurring problem patterns and their root-causes and mine suggestions to solve the identified problems. We predict the occurrence of different problems, (with an accuracy of ≈ 80%) at different locations and times leveraging the analysis done above along with weather information and news reports. In addition, we design a feature-packed visualization that significantly enhances the ability to analyse data in real-time.

Identifying User Intent and Context in Graph Queries.

This paper serves as a report for the participation of Special Interest Group In Data (SIGDATA), Indian Institute of Technology, Kanpur in the String Similarity Workshop, EDBT, 2013. We present a novel technique to efficiently process edit distance based string similarity queries. Our technique draws upon some previously conducted works in the field and introduces new methods to tackle the issues therein. We focus on achieving minimum possible execution time while being rather liberal with memory consumption. We propose and support the use of deletion neighborhoods for fast edit distance lookups in dictionaries. Our work emphasizes the power of deletion neighborhoods over other popular finger print based schemes for similarity search queries. Furthermore, we establish that it is possible to reduce the large space requirement of a deletion neighborhood based finger print scheme using simple hashing techniques, thereby making the scheme suitable for practical application. We compare our implementation with the state of the art libraries (Flamingo) and report speed ups of up to an order of magnitude.