arXiv subjects

Tanmoy Chakraborty

Publications and source records attributed to Tanmoy Chakraborty.

At least 271 records · Page 15Linked to original sources

arXiv2017

Polarizing the electronic and nuclear spin of the NV-center in diamond in arbitrary magnetic fields: analysis of the optical pumping process

Initializing a set of qubits to a given quantum state is a basic prerequisite for the physical implementation of quantum-information protocols. Here, we discuss the polarization of the electronic and nuclear spin in a single nitrogen vacancy center in diamond. Our initialization scheme uses a sequence of laser, microwave and radio-frequency pulses, and we optimize the pumping parameters of the laser pulse. A rate equation model is formulated that explains the effect of the laser pulse on the spin system. We have experimentally determined the population of the relevant spin states as a function of the duration of the laser pulse by measuring Rabi oscillations and Ramsey-type free-induction decays. The experimental data have been analyzed to determine the pumping rates of the rate equation model.

quant-ph↗

arXiv2017

Determining sentiment in citation text and analyzing its impact on the proposed ranking index

Whenever human beings interact with each other, they exchange or express opinions, emotions, and sentiments. These opinions can be expressed in text, speech or images. Analysis of these sentiments is one of the popular research areas of present day researchers. Sentiment analysis, also known as opinion mining tries to identify or classify these sentiments or opinions into two broad categories - positive and negative. In recent years, the scientific community has taken a lot of interest in analyzing sentiment in textual data available in various social media platforms. Much work has been done on social media conversations, blog posts, newspaper articles and various narrative texts. However, when it comes to identifying emotions from scientific papers, researchers have faced some difficulties due to the implicit and hidden nature of opinion. By default, citation instances are considered inherently positive in emotion. Popular ranking and indexing paradigms often neglect the opinion present while citing. In this paper, we have tried to achieve three objectives. First, we try to identify the major sentiment in the citation text and assign a score to the instance. We have used a statistical classifier for this purpose. Secondly, we have proposed a new index (we shall refer to it hereafter as M-index) which takes into account both the quantitative and qualitative factors while scoring a paper. Thirdly, we developed a ranking of research papers based on the M-index. We also try to explain how the M-index impacts the ranking of scientific papers.

cs.IR↗

arXiv2016

$C^3$-index: A PageRank based multi-faceted metric for authors' performance measurement

Ranking scientific authors is an important but challenging task, mostly due to the dynamic nature of the evolving scientific publications. The basic indicators of an author's productivity and impact are still the number of publications and the citation count (leading to the popular metrics such as h-index, g-index etc.). H-index and its popular variants are mostly effective in ranking highly-cited authors, thus fail to resolve ties while ranking medium-cited and low-cited authors who are majority in number. Therefore, these metrics are inefficient to predict the ability of promising young researchers at the beginning of their career. In this paper, we propose $C^3$-index that combines the effect of citations and collaborations of an author in a systematic way using a weighted multi-layered network to rank authors. We conduct our experiments on a massive publication dataset of Computer Science and show that - (i) $C^3$-index is consistent over time, which is one of the fundamental characteristics of a ranking metric, (ii) $C^3$-index is as efficient as h-index and its variants to rank highly-cited authors, (iii) $C^3$-index can act as a conflict resolution metric to break ties in the ranking of medium-cited and low-cited authors, (iv) $C^3$-index can also be used to predict future achievers at the early stage of their career.

cs.DL↗

arXiv2016

Ensemble-Based Algorithms to Detect Disjoint and Overlapping Communities in Networks

Given a set ${\cal AL}$ of community detection algorithms and a graph $G$ as inputs, we propose two ensemble methods $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ that (respectively) identify disjoint and overlapping communities in $G$. $\mathtt{EnDisCO}$ transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. $\mathtt{MeDOC}$ groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performance. To the best of our knowledge, $\mathtt{MeDOC}$ is the first ensemble approach for overlapping community detection.

cs.SI↗

arXiv2016

All Fingers are not Equal: Intensity of References in Scientific Articles

Research accomplishment is usually measured by considering all citations with equal importance, thus ignoring the wide variety of purposes an article is being cited for. Here, we posit that measuring the intensity of a reference is crucial not only to perceive better understanding of research endeavor, but also to improve the quality of citation-based applications. To this end, we collect a rich annotated dataset with references labeled by the intensity, and propose a novel graph-based semi-supervised model, GraLap to label the intensity of references. Experiments with AAN datasets show a significant improvement compared to the baselines to achieve the true labels of the references (46% better correlation). Finally, we provide four applications to demonstrate how the knowledge of reference intensity leads to design better real-world applications.

cs.CL↗

arXiv2016

Is this conference a top-tier? ConfAssist: An assistive conflict resolution framework for conference categorization

Classifying publication venues into top-tier or non top-tier is quite subjective and can be debatable at times. In this paper, we propose ConfAssist, a novel assisting framework for conference categorization that aims to address the limitations in the existing systems and portals for venue classification. We start with the hypothesis that top-tier conferences are much more stable than other conferences and the inherent dynamics of these groups differs to a very large extent. We identify various features related to the stability of conferences that might help us separate a top-tier conference from the rest of the lot. While there are many clear cases where expert agreement can be almost immediately achieved as to whether a conference is a top-tier or not, there are equally many cases that can result in a conflict even among the experts. ConfAssist tries to serve as an aid in such cases by increasing the confidence of the experts in their decision. An analysis of 110 conferences from 22 sub-fields of computer science clearly favors our hypothesis as the top-tier conferences are found to exhibit much less fluctuations in the stability related features than the non top-tier ones. We evaluate our hypothesis using systems based on conference categorization. For the evaluation, we conducted human judgment survey with 28 domain experts. The results are impressive with 85.18% classification accuracy. We also compare the dynamics of the newly started conferences with the older conferences to identify the initial signals of popularity. The system is applicable to any conference with atleast 5 years of publication history.

cs.SI↗

arXiv2016

Permanence and Community Structure in Complex Networks

The goal of community detection algorithms is to identify densely-connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily driven to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this paper, we argue that the belongingness of nodes in a community is not uniform. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors: (i) the the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We discuss how permanence can help us understand and utilize the structure and evolution of communities by demonstrating that it can be used to -- (i) measure the persistence of a vertex in a community, (ii) design strategies to strengthen the community structure, (iii) explore the core-periphery structure within a community, and (iv) select suitable initiators for message spreading. We demonstrate that the process of maximizing permanence produces meaningful communities that concur with the ground-truth community structure of the networks more accurately than eight other popular community detection algorithms. Finally, we show that the communities obtained by this method are (i) less affected by the changes in vertex-ordering, and (ii) more resilient to resolution limit, degeneracy of solutions and asymptotic growth of values.

cs.SI↗

arXiv2016

Metrics for Community Analysis: A Survey

Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms - disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

cs.SI↗

arXiv2016

GenPerm: A Unified Method for Detecting Non-overlapping and Overlapping Communities

Detection of non-overlapping and overlapping communities are essentially the same problem. However, current algorithms focus either on finding overlapping or non-overlapping communities. We present a generalized framework that can identify both non-overlapping and overlapping communities, without any prior input about the network or its community distribution. To do so, we introduce a vertex-based metric, GenPerm, that quantifies by how much a vertex belongs to each of its constituent communities. Our community detection algorithm is based on maximizing the GenPerm over all the vertices in the network. We demonstrate, through experiments over synthetic and real-world networks, that GenPerm is more effective than other metrics in evaluating community structure. Further, we show that due to its vertex-centric property, GenPerm can be used to unfold several inferences beyond community detection, such as core-periphery analysis and message spreading. Our algorithm for maximizing GenPerm outperforms six state-of-the-art algorithms in accurately predicting the ground-truth labels. Finally, we discuss the problem of resolution limit in overlapping communities and demonstrate that maximizing GenPerm can mitigate this problem.

cs.SI↗

arXiv2016

$C^3$-index: Revisiting Authors' Performance Measure

Author performance indices (such as h-index and its variants) fail to resolve ties while ranking authors with low index values (majority in number) which includes the young researchers. In this work we leverage the citations as well as collaboration profile of an author in a novel way using a weighted multi-layered network and propose a variant of page-rank algorithm to obtain a new author performance measure, $C^3$-index. Experiments on a massive publication dataset reveal several interesting characteristics of our metric: (i) we observe that $C^3$-index is consistent over time, (ii) $C^3$-index has high potential to break ties among low rank authors, (iii) $C^3$-index can effectively be used to predict future achievers at the early stage of their career.

cs.DL↗

arXiv2016

On the Discovery of Success Trajectories of Authors

Understanding the qualitative patterns of research endeavor of scientific authors in terms of publication count and their impact (citation) is important in order to quantify success trajectories. Here, we examine the career profile of authors in computer science and physics domains and discover at least six different success trajectories in terms of normalized citation count in longitudinal scale. Initial observations of individual trajectories lead us to characterize the authors in each category. We further leverage this trajectory information to build a two-stage stratification model to predict future success of an author at the early stage of her career. Our model outperforms the baseline with an average improvement of 15.68% for both the datasets.

cs.DL↗

arXiv2015

On the Formation of Circles in Co-authorship Networks

The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of "circles", which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks. In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -- two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AUC (Area under the ROC) and P rec@20 (Precision at Top 20) respectively compared to the case, where the circle information is not present.

cs.SI↗

arXiv2015

Leveraging disjoint communities for detecting overlapping community structure

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network. In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.

cs.SI↗

arXiv2015

On the categorization of scientific citation profiles in computer sciences

A common consensus in the literature is that the citation profile of published articles in general follows a universal pattern - an initial growth in the number of citations within the first two to three years after publication followed by a steady peak of one to two years and then a final decline over the rest of the lifetime of the article. This observation has long been the underlying heuristic in determining major bibliometric factors such as the quality of a publication, the growth of scientific communities, impact factor of publication venues etc. In this paper, we gather and analyze a massive dataset of scientific papers from the computer science domain and notice that the citation count of the articles over the years follows a remarkably diverse set of patterns - a profile with an initial peak (PeakInit), with distinct multiple peaks (PeakMul), with a peak late in time (PeakLate), that is monotonically decreasing (MonDec), that is monotonically increasing (MonIncr) and that can not be categorized into any of the above (Oth). We conduct a thorough experiment to investigate several important characteristics of these categories such as how individual categories attract citations, how the categorization is influenced by the year and the venue of publication of papers, how each category is affected by self-citations, the stability of the categories over time, and how much each of these categories contribute to the core of the network. Further, we show that the traditional preferential attachment models fail to explain these citation profiles. Therefore, we propose a novel dynamic growth model that takes both the preferential attachment and the aging factor into account in order to replicate the real-world behavior of various citation profiles. We believe that this paper opens the scope for a serious re-investigation of the existing bibliometric indices for scientific research.

cs.DL↗

arXiv2014

Experimental Evidences of Quantum Phase Transition in a Spin Cluster Compound

Experimental realization of magnetic field induced quantum phase transition (QPT) is reported for NH4CuPO4.H2O, a two spin cluster material with isotropic Heisenberg interaction. Experimental magnetization and specific heat data have been collected as a function of temperature and magnetic field. Experimental data have been analyzed in terms of Heisenberg dimer model. Two quantum complementary observables representing local and non-local properties of the spins are constructed using the experimental data and a clear evidence of QPT is observed through partial quantum information sharing when the magnetic field is swept through a particular value. Signature of QPT is also observed when specific heat is measured as a function of magnetic field at low temperature. Furthermore, using the experimental specific heat data, magnetic energy values are calculated and their variations are captured as a function of magnetic field and temperature.

cond-mat.str-el↗

arXiv2014

On the Permanence of Vertices in Network Communities

Despite the prevalence of community detection algorithms, relatively less work has been done on understanding whether a network is indeed modular and how resilient the community structure is under perturbations. To address this issue, we propose a new vertex-based metric called "permanence", that can quantitatively give an estimate of the community-like structure of the network. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon the following two factors: (i) the distribution of external connectivity of the vertex to individual communities and not the total external connectivity, and (ii) the strength of its internal connectivity and not just the total internal edges. In this paper, we demonstrate that compared to other metrics, permanence provides (i) a more accurate estimate of a derived community structure to the ground-truth community and (ii) is more sensitive to perturbations in the network. As a by-product of this study, we have also developed a community detection algorithm based on maximizing permanence. For a modular network structure, the results of our algorithm match well with ground-truth communities.

physics.soc-ph↗

arXiv2014

An effective AHP-based metaheuristic approach to solve supplier selection problem

The supplier selection problem is based on electing the best supplier from a group of pre-specified candidates, is identified as a Multi Criteria Decision Making (MCDM), is proportionately significant in terms of qualitative and quantitative attributes. It is a fundamental issue to achieve a trade-off between such quantifiable and unquantifiable attributes with an aim to accomplish the best solution to the abovementioned problem. This article portrays a metaheuristic based optimization model to solve this NP-Complete problem. Initially the Analytic Hierarchy Process (AHP) is implemented to generate an initial feasible solution of the problem. Thereafter a Simulated Annealing (SA) algorithm is exploited to improve the quality of the obtained solution. The Taguchi robust design method is exploited to solve the critical issues on the subject of the parameter selection of the SA technique. In order to verify the proposed methodology the numerical results are demonstrated based on tangible industry data.

cs.NE↗

arXiv2014

Identifying Bengali Multiword Expressions using Semantic Clustering

One of the key issues in both natural language understanding and generation is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge problem to the precise language processing due to their idiosyncratic nature and diversity in lexical, syntactical and semantic properties. The semantics of a MWE cannot be expressed after combining the semantics of its constituents. Therefore, the formalism of semantic clustering is often viewed as an instrument for extracting MWEs especially for resource constraint languages like Bengali. The present semantic clustering approach contributes to locate clusters of the synonymous noun tokens present in the document. These clusters in turn help measure the similarity between the constituent words of a potentially candidate phrase using a vector space model and judge the suitability of this phrase to be a MWE. In this experiment, we apply the semantic clustering approach for noun-noun bigram MWEs, though it can be extended to any types of MWEs. In parallel, the well known statistical models, namely Point-wise Mutual Information (PMI), Log Likelihood Ratio (LLR), Significance function are also employed to extract MWEs from the Bengali corpus. The comparative evaluation shows that the semantic clustering approach outperforms all other competing statistical models. As a by-product of this experiment, we have started developing a standard lexicon in Bengali that serves as a productive Bengali linguistic thesaurus.

cs.CL↗