arXiv subjects

Soumya Sarkar

Publications and source records attributed to Soumya Sarkar.

29 records · Page 2Linked to original sources

arXiv2020

Hate begets Hate: A Temporal Study of Hate Speech

With the ongoing debate on 'freedom of speech' vs. 'hate speech' there is an urgent need to carefully understand the consequences of the inevitable culmination of the two, i.e., 'freedom of hate speech' over time. An ideal scenario to understand this would be to observe the effects of hate speech in an (almost) unrestricted environment. Hence, we perform the first temporal analysis of hate speech on Gab.com, a social media site with very loose moderation policy. We first generate temporal snapshots of Gab from millions of posts and users. Using these temporal snapshots, we compute an activity vector based on DeGroot model to identify hateful users. The amount of hate speech in Gab is steadily increasing and the new users are becoming hateful at an increased and faster rate. Further, our analysis analysis reveals that the hate users are occupying the prominent positions in the Gab network. Also, the language used by the community as a whole seem to correlate more with that of the hateful users as compared to the non-hateful ones. We discuss how, many crucial design questions in CSCW open up from our work.

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arXiv2019

StRE: Self Attentive Edit Quality Prediction in Wikipedia

Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing towards developing automated tools for content moderation and review. In this paper we propose Self Attentive Revision Encoder (StRE) which leverages orthographic similarity of lexical units toward predicting the quality of new edits. In contrast to existing propositions which primarily employ features like page reputation, editor activity or rule based heuristics, we utilize the textual content of the edits which, we believe contains superior signatures of their quality. More specifically, we deploy deep encoders to generate representations of the edits from its text content, which we then leverage to infer quality. We further contribute a novel dataset containing 21M revisions across 32K Wikipedia pages and demonstrate that StRE outperforms existing methods by a significant margin at least 17% and at most 103%. Our pretrained model achieves such result after retraining on a set as small as 20% of the edits in a wikipage. This, to the best of our knowledge, is also the first attempt towards employing deep language models to the enormous domain of automated content moderation and review in Wikipedia.

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arXiv2018

On Rich Clubs of Path-Based Centralities in Networks

Many scale-free networks exhibit a rich club structure, where high degree vertices form tightly interconnected subgraphs. In this paper, we explore the emergence of rich clubs in the context of shortest path based centrality metrics. We term these subgraphs of connected high closeness or high betweeness vertices as rich centrality clubs (RCC). Our experiments on real world and synthetic networks highlight the inter-relations between RCCs, expander graphs, and the core-periphery structure of the network. We show empirically and theoretically that RCCs exist, if the core-periphery structure of the network is such that each shell is an expander graph, and their density decreases from inner to outer shells. The main contributions of our paper are: (i) we demonstrate that the formation of RCC is related to the core-periphery structure and particularly the expander like properties of each shell, (ii) we show that the RCC property can be used to find effective seed nodes for spreading information and for improving the resilience of the network under perturbation and, finally, (iii) we present a modification algorithm that can insert RCC within networks, while not affecting their other structural properties. Taken together, these contributions present one of the first comprehensive studies of the properties and applications of rich clubs for path based centralities.

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arXiv2018

Core2Vec: A core-preserving feature learning framework for networks

Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogues of skip-gram model in graphs define a notion of neighbourhood and aim to find the vector representation for a node, which maximizes the likelihood of preserving this neighborhood. In this paper, we take a drastic departure from the existing notion of neighbourhood of a node by utilizing the idea of coreness. More specifically, we utilize the well-established idea that nodes with similar core numbers play equivalent roles in the network and hence induce a novel and an organic notion of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic framework for learning low dimensional continuous feature mapping for a node. Consequently, the nodes having similar core numbers are relatively closer in the vector space that we learn. We further demonstrate the effectiveness of core2vec by comparing word similarity scores obtained by our method where the node representations are drawn from standard word association graphs against scores computed by other state-of-the-art network representation techniques like node2vec, DeepWalk and LINE. Our results always outperform these existing methods

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arXiv2018

Using Core-Periphery Structure to Predict High Centrality Nodes in Time-Varying Networks

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree.

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arXiv2018

Simulation study of elliptic flow of charged hadrons produced in Au + Au collisions at the Facility for Antiproton and Ion Research

Centrality and system geometry dependence of azimuthal anisotropy of charged hadrons measured in terms of the elliptic flow parameter are investigated using Au+Au event samples at incident beam energy $20$A GeV and $40$A GeV generated by Ultrarelativistic Quantum Molecular Dynamics and A Multiphase Transport models. The Monte Carlo Glauber model is employed to estimate the eccentricity of the overlapping zone at an early stage of the collisions. Anisotropies present both in the particle multiplicity distribution and in the kinetic radial expansion are examined by using standard statistical and phenomenological methods. In the context of upcoming Compressed Baryonic Matter experiment to be held at the Facility for Antiproton and Ion Research, the present set of simulated results provide us not only with an opportunity to examine the expected collective behavior of hadronic matter at high baryon density and moderate temperature environment, but when compared with similar results obtained from Relativistic Heavy Ion Collider and Large Hadron Collider experiments, they also allow us to investigate how anisotropy of hadronic matter may differ or agree with its low baryon density and high temperature counterpart.

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arXiv2018

Azimuthal anisotropy in particle distribution in A Multi-phase Transport Model

Anisotropic flow of hadronic matter is considered as a sensitive tool to detect the early stage dynamics of high-energy heavy-ion collisions. Taking the event by event fluctuations of the collision geometry into account, the elliptic flow parameter and the triangular flow parameter derived from the azimuthal distribution of produced hadrons, are investigated within the framework of a multiphase transport (AMPT) model, at a collision energy that in near future will typically be available at the Facility for Antiproton and Ion Research. The dependence of elliptic and triangular flow parameters on initial fluctuations, on parton scattering cross-sections, their mass ordering on different hadron species and on the constituent quark number scaling are examined. The AMPT simulation can not exactly match the elliptic flow results on Pb + Pb collision at 40A GeV of the NA49 experiment. The simulation results presented in this work are expected to provide us with an insight to study flow properties at high baryonic density but moderate temperature, and also with an opportunity to compare similar results available from RHIC and LHC experiments.

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arXiv2018

Radial flow in a multi phase transport model at FAIR energies

Azimuthal distributions of radial velocities of charged hadrons produced in nucleus-nucleus $(AB)$ collisions are compared with the corresponding azimuthal distribution of charged hadron multiplicity in the framework of a multiphase transport (AMPT) model at two different collision energies. The mean radial velocity seems to be a good probe for studying radial expansion. While the anisotropic part of the distributions indicates a kind of collective nature of radial expansion, the isotropic part characterizes a thermal motion. The present investigation is carried out keeping the upcoming Compressed Baryonic Matter (CBM) experiment to be held at the Facility for Anti-proton Ion Research (FAIR) in mind. As far as high-energy heavy-ion interactions are concerned, CBM will supplement the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) experiments. In this context our simulation results at high baryochemical potential would be interesting, when scrutinized from the perspective of almost a baryon free environment achieved at RHIC and LHC.

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arXiv2018

ComPAS: Community Preserving Sampling for Streaming Graphs

In the era of big data, graph sampling is indispensable in many settings. Existing sampling methods are mostly designed for static graphs, and aim to preserve basic structural properties of the original graph (such as degree distribution, clustering coefficient etc.) in the sample. We argue that for any sampling method it is impossible to produce an universal representative sample which can preserve all the properties of the original graph; rather sampling should be application specific (such as preserving hubs - needed for information diffusion). Here we consider community detection as an application scenario. We propose ComPAS, a novel sampling strategy that unlike previous methods, is not only designed for streaming graphs (which is a more realistic representation of a real-world scenario) but also preserves the community structure of the original graph in the sample. Empirical results on both synthetic and different real-world graphs show that ComPAS is the best to preserve the underlying community structure with average performance reaching 73.2% of the most informed algorithm for static graphs.

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arXiv2016

Understanding Stability of Noisy Networks through Centrality Measures and Local Connections

Networks created from real-world data contain some inaccuracies or noise, manifested as small changes in the network structure. An important question is whether these small changes can significantly affect the analysis results. In this paper, we study the effect of noise in changing ranks of the high centrality vertices. We compare, using the Jaccard Index (JI), how many of the top-k high centrality nodes from the original network are also part of the top-k ranked nodes from the noisy network. We deem a network as stable if the JI value is high. We observe two features that affect the stability. First, the stability is dependent on the number of top-ranked vertices considered. When the vertices are ordered according to their centrality values, they group into clusters. Perturbations to the network can change the relative ranking within the cluster, but vertices rarely move from one cluster to another. Second, the stability is dependent on the local connections of the high ranking vertices. The network is highly stable if the high ranking vertices are connected to each other. Our findings show that the stability of a network is affected by the local properties of high centrality vertices, rather than the global properties of the entire network. Based on these local properties we can identify the stability of a network, without explicitly applying a noise model.

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arXiv2016

Sensitivity and Reliability in Incomplete Networks: Centrality Metrics to Community Scoring Functions

Network analysis is an important tool in understanding the behavior of complex systems of interacting entities. However, due to the limitations of data gathering technologies, some interactions might be missing from the network model. This is a ubiquitous problem in all domains that use network analysis, from social networks to hyper-linked web networks to biological networks. Consequently, an important question in analyzing networks is to understand how increasing the noise level (i.e. percentage of missing edges) affects different network parameters. In this paper we evaluate the effect of noise on community scoring and centrality-based parameters with respect to two different aspects of network analysis: (i) sensitivity, that is how the parameter value changes as edges are removed and (ii) reliability in the context of message spreading, that is how the time taken to broadcast a message changes as edges are removed. Our experiments on synthetic and real-world networks and three different noise models demonstrate that for both the aspects over all networks and all noise models, permanence qualifies as the most effective metric. For the sensitivity experiments closeness centrality is a close second. For the message spreading experiments, closeness and betweenness centrality based initiator selection closely competes with permanence. This is because permanence has a dual characteristic where the cumulative permanence over all vertices is sensitive to noise but the ids of the top-rank vertices, which are used to find seeds during message spreading remain relatively stable under noise.

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