OpenRaiser/Intern-Atlas huggingface dataset:
CrimeBB: Enabling Cybercrime Research on Underground Forums at Scale
Authors: Sergio Pastrana, Daniel R. Thomas, Alice Hutchings, Richard Clayton
Venue: Conference WWW 2018
Year: 2018

Underground forums allow criminals to interact, exchange knowledge, and trade in products and services. They also provide a pathway into cybercrime, tempting the curious to join those already motivated to obtain easy money. Analysing these forums enables us to better understand the behaviours of offenders and pathways into crime. Prior research has been valuable, but limited by a reliance on datasets that are incomplete or outdated. More complete data, going back many years, allows for comprehensive research into the evolution of forums and their users. We describe CrimeBot, a crawler designed around the particular challenges of capturing data from underground forums. CrimeBot is used to update and maintain CrimeBB, a dataset of more than 48m posts made from 1m accounts in 4 different operational forums over a decade. This dataset presents a new opportunity for large-scale and longitudinal analysis using up-to-date information. We illustrate the potential by presenting a case study using CrimeBB, which analyses which activities lead new actors into engagement with cybercrime. CrimeBB is available to other academic researchers under a legal agreement, designed to prevent misuse and provide safeguards for ethical research.
An in-depth study of mobile browser performance
Authors: Javad Nejati, Aruna Balasubramanian
Venue: Conference WWW 2016
Year: 2016

Mobile page load times are an order of magnitude slower compared to non-mobile pages. It is not clear what causes the poor performance: the slower network, the slower computational speeds, or other reasons. Further, most Web optimizations are designed for non-mobile browsers and do not translate well to the mobile browser. Towards understanding mobile Web page load times, in this paper we: (1) perform an in-depth pairwise comparison of loading a page on a mobile versus a non-mobile browser, and (2) characterize the bottlenecks in the mobile browser {\em vis-a-vis} non-mobile browsers. To this end, we build a testbed that allows us to directly compare the low-level page load activities and bottlenecks when loading a page on a mobile versus a non-mobile browser. We find that computation is the main bottleneck when loading a page on mobile browsers. This is in contrast to non-mobile browsers where networking is the main bottleneck. We also find that the composition of the critical path during page load is different when loading pages on the mobile versus the non-mobile browser. A key takeaway of our work is that we need to fundamentally rethink optimizations for mobile browsers.
Dissecting performance of production QUIC
Authors: Alexander Yu, Theophilus A. Benson
Venue: Conference WWW 2021
Year: 2021

IETF QUIC, the standardized version of Google’s UDP-based layer-4 network protocol, has seen increasing adoption from large Internet companies for its benefits over TCP. Yet despite its rapid adoption, performance analysis of QUIC in production is scarce. Most existing analyses have only used unoptimized open-source QUIC servers on non-tuned kernels: these analyses are unrepresentative of production deployments which raises the question of whether QUIC actually outperforms TCP in practice. In this paper, we conduct one of the first comparative studies on the performance of QUIC and TCP against production endpoints hosted by Google, Facebook, and Cloudflare under various dimensions: network conditions, workloads, and client implementations. To understand our results, we create a tool to systematically visualize the root causes of performance differences between the two protocols. Using our tool we make several key observations. First, while QUIC has some inherent advantages over TCP, such as worst-case 1-RTT handshakes, its overall performance is largely determined by the server’s choice of congestion-control algorithm and the robustness of its congestion-control implementation under edge-case network scenarios. Second, we find that some QUIC clients require non-trivial configuration tuning in order to achieve optimal performance. Lastly, we demonstrate that QUIC’s removal of head-of-line (HOL) blocking has little impact on web-page performance in practice. Taken together, our observations illustrate the fact that QUIC’s performance is inherently tied to implementation design choices, bugs, and configurations which implies that QUIC measurements are not always a reflection of the protocol and often do not generalize across deployments.
Hits on the web: How does it compare?
Authors: Marc Najork, Hugo Zaragoza, Michael J. Taylor
Venue: Conference SIGIR 2007
Year: 2007

This paper describes a large-scale evaluation of the effectiveness of HITS in comparison with other link-based ranking algorithms, when used in combination with a state-of-the-art text retrieval algorithm exploiting anchor text. We quantified their effectiveness using three common performance measures: the mean reciprocal rank, the mean average precision, and the normalized discounted cumulative gain measurements. The evaluation is based on two large data sets: a breadth-first search crawl of 463 million web pages containing 17.6 billion hyperlinks and referencing 2.9 billion distinct URLs; and a set of 28,043 queries sampled from a query log, each query having on average 2,383 results, about 17 of which were labeled by judges. We found that HITS outperforms PageRank, but is about as effective as web-page in-degree. The same holds true when any of the link-based features are combined with the text retrieval algorithm. Finally, we studied the relationship between query specificity and the effectiveness of selected features, and found that link-based features perform better for general queries, whereas BM25F performs better for specific queries.
Cookie swap party: Abusing first-party cookies for web tracking
Authors: Quan Chen, Panagiotis Ilia, Michalis Polychronakis, Alexandros Kapravelos
Venue: Conference WWW 2021
Year: 2021

As a step towards protecting user privacy, most web browsers perform some form of third-party HTTP cookie blocking or periodic deletion by default, while users typically have the option to select even stricter blocking policies. As a result, web trackers have shifted their efforts to work around these restrictions and retain or even improve the extent of their tracking capability. In this paper, we shed light into the increasingly used practice of relying on first-party cookies that are set by third-party JavaScript code to implement user tracking and other potentially unwanted capabilities. Although unlike third-party cookies, first-party cookies are not sent automatically by the browser to third-parties on HTTP requests, this tracking is possible because any included third-party code runs in the context of the parent page, and thus can fully set or read existing first-party cookies—which it can then leak to the same or other third parties. Previous works that survey user privacy on the web in relation to cookies, third-party or otherwise, have not fully explored this mechanism. To address this gap, we propose a dynamic data flow tracking system based on Chromium to track the leakage of first-party cookies to third parties, and used it to conduct a large-scale study of the Alexa top 10K websites. In total, we found that 97.72% of the websites have first-party cookies that are set by third-party JavaScript, and that on 57.66% of these websites there is at least one such cookie that contains a unique user identifier that is diffused to multiple third parties. Our results highlight the privacy-intrusive capabilities of first-party cookies, even when a privacy-savvy user has taken mitigative measures such as blocking third-party cookies, or employing popular crowd-sourced filter lists such as EasyList/EasyPrivacy and the Disconnect list.
Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos
Authors: Quyu Kong, Marian-Andrei Rizoiu, Siqi Wu, Lexing Xie
Venue: Conference WWW 2018
Year: 2018

What makes content go viral Which videos become popular and why others don't Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these theoretical results. HIPie--an interactive visualization system--is created to fill this gap, by enabling users to reason about the virality and the popularity of online videos. It retrieves the metadata and the past popularity series of Youtube videos, it employs the Hawkes Intensity Process, a state-of-the-art online popularity model for explaining and predicting video popularity, and it presents videos comparatively in a series of interactive plots. This system will help both content consumers and content producers in a range of data-driven inquiries, such as to comparatively analyze videos and channels, to explain and to predict future popularity, to identify viral videos, and to estimate responses to online promotion.
Scalable Subgraph Counting: The Methods Behind The Madness
Authors: Seshadhri Comandur, Srikanta Tirthapura
Venue: Conference WWW 2019
Year: 2019

Subgraph counting is a fundamental problem in graph analysis that finds use in a wide array of applications. The basic problem is to count or approximate the occurrences of a small subgraph (the pattern) in a large graph (the dataset). Subgraph counting is a computationally challenging problem, and the last few years have seen a rich literature develop around scalable solutions for it. However, these results have thus far appeared as a disconnected set of ideas that are applied separately by different research groups. We observe that there are a few common algorithmic building blocks that most subgraph counting results build on. In this tutorial, we attempt to summarize current methods through distilling these basic algorithmic building blocks. The tutorial will also cover methods for subgraph analysis on “big data” computational models such as the streaming model and models of parallel and distributed computation.
Listing k-cliques in sparse real-world graphs
Authors: Maximilien Danisch, Oana Balalau, Mauro Sozio
Venue: Conference WWW 2018
Year: 2018

Motivated by recent studies in the data mining community which require to efficiently list all k-cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficient parallel algorithm for such a problem. Our theoretical analysis provides the best asymptotic upper bound on the running time of our algorithm for the case when the input graph is sparse. Our experimental evaluation on large real-world graphs shows that our parallel algorithm is faster than state-of-the-art algorithms, while boasting an excellent degree of parallelism. In particular, we are able to list all k-cliques (for any k) in graphs containing up to tens of millions of edges as well as all $10$-cliques in graphs containing billions of edges, within a few minutes and a few hours respectively. Finally, we show how our algorithm can be employed as an effective subroutine for finding the k-clique core decomposition and an approximate k-clique densest subgraphs in very large real-world graphs.
Lightning Fast and Space Efficient k-clique Counting
Authors: Xiaowei Ye, Rong-Hua Li, Qiangqiang Dai, Hongzhi Chen, Guoren Wang
Venue: Conference WWW 2022
Year: 2022

K-clique counting is a fundamental problem in network analysis which has attracted much attention in recent years. Computing the count of k-cliques in a graph for a large k (e.g., k = 8) is often intractable as the number of k-cliques increases exponentially w.r.t. (with respect to) k. Existing exact k-clique counting algorithms are often hard to handle large dense graphs, while sampling-based solutions either require a huge number of samples or consume very high storage space to achieve a satisfactory accuracy. To overcome these limitations, we propose a new framework to estimate the number of k-cliques which integrates both the exact k-clique counting technique and two novel color-based sampling techniques. The key insight of our framework is that we only apply the exact algorithm to compute the k-clique counts in the sparse regions of a graph, and use the proposed sampling-based techniques to estimate the number of k-cliques in the dense regions of the graph. Specifically, we develop two novel dynamic programming based k-color set sampling techniques to efficiently estimate the k-clique counts, where a k-color set contains k nodes with k different colors. Since a k-color set is often a good approximation of a k-clique in the dense regions of a graph, our sampling-based solutions are extremely efficient and accurate. Moreover, the proposed sampling techniques are space efficient which use near-linear space w.r.t. graph size. We conduct extensive experiments to evaluate our algorithms using 8 real-life graphs. The results show that our best algorithm is at least one order of magnitude faster than the state-of-the-art sampling-based solutions (with the same relative error 0.1%) and can be up to three orders of magnitude faster than the state-of-the-art exact algorithm on large graphs.
Framework for Bias Detection in Machine Learning Models: A Fairness Approach
Authors: Alveiro Alonso Rosado Gomez, Maritza Liliana Calderón-Benavides
Venue: Conference WSDM 2024
Year: 2024

The research addresses bias and inequity in binary classification problems in machine learning. Despite existing ethical frameworks for artificial intelligence, detailed guidance on practices and tech niques to address these issues is lacking. The main objective is to identify and analyze theoretical and practical components related to the detection and mitigation of biases and inequalities in machine learning. The proposed approach combines best practices, ethics, and technology to promote the responsible use of artificial intelligence in Colombia. The methodology covers the definition of performance and fairness interests, interventions in preprocessing, processing, and post-processing, and the generation of recommendations and explainability of the model.
Understanding user sensemaking in machine learning fairness assessment systems
Authors: Ziwei Gu, Jing Nathan Yan, Jeffrey M. Rzeszotarski
Venue: Conference WWW 2021
Year: 2021

A variety of systems have been proposed to assist users in detecting machine learning (ML) fairness issues. These systems approach bias reduction from a number of perspectives, including recommender systems, exploratory tools, and dashboards. In this paper, we seek to inform the design of these systems by examining how individuals make sense of fairness issues as they use different de-biasing affordances. In particular, we consider the tension between de-biasing recommendations which are quick but may lack nuance and ”what-if” style exploration which is time consuming but may lead to deeper understanding and transferable insights. Using logs, think-aloud data, and semi-structured interviews we find that exploratory systems promote a rich pattern of hypothesis generation and testing, while recommendations deliver quick answers which satisfy participants at the cost of reduced information exposure. We highlight design requirements and trade-offs in the design of ML fairness systems to promote accurate and explainable assessments.
A Unified Invariant Learning Framework for Graph Classification
Authors: Yongduo Sui, Jie Sun, Shuyao Wang, Zemin Liu, Qing Cui, Longfei Li, Xiang Wang
Venue: Conference KDD 2025
Year: 2025

Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize stable features in graph data for classification, based on the premise that these features causally determine the target label, and their influence is invariant to changes in distribution. Along this line, most studies have attempted to pinpoint these stable features by emphasizing explicit substructures in the graph, such as masked or attentive subgraphs, and primarily enforcing the invariance principle in the semantic space, i.e., graph representations. However, we argue that focusing only on the semantic space may not accurately identify these stable features. To address this, we introduce the Unified Invariant Learning (UIL) framework for graph classification. It provides a unified perspective on invariant graph learning, emphasizing both structural and semantic invariance principles to identify more robust stable features. In the graph space, UIL adheres to the structural invariance principle by reducing the distance between graphons over a set of stable features across different environments. Simultaneously, to confirm semantic invariance, UIL underscores that the acquired graph representations should demonstrate exemplary performance across diverse environments. We present both theoretical and empirical evidence to confirm our method's ability to recognize superior stable features. Moreover, through a series of comprehensive experiments complemented by in-depth analyses, we demonstrate that UIL considerably enhances OOD generalization, surpassing the performance of leading baseline methods. Our codes are available at https://github.com/yongduosui/UIL.
ReFound: Crafting a Foundation Model for Urban Region Understanding upon Language and Visual Foundations
Authors: Congxi Xiao, Jingbo Zhou 0003, Yixiong Xiao, Jizhou Huang, Hui Xiong 0001
Venue: Conference KDD 2024
Year: 2024

Understanding urban regional characteristics is pivotal in driving critical insights for urban planning and management. We have witnessed the successful application of pre-trained Foundation Models (FMs) in generating universal representations for various downstream tasks. However, applying this principle to the geospatial domain remains challenging, primarily due to the difficulty of gathering extensive data for developing a dedicated urban foundation model. Though there have been some attempts to empower the existing FMs with urban data, most of them focus on single-modality FMs without considering the multi-modality nature of urban region understanding tasks. To address this gap, we introduce ReFound - a novel framework for <u>Re</u>-training a <u>Found</u>ation model for urban region understanding, harnessing the strengths of both language and visual FMs. In this framework, we first invent a Mixture-of-Geospatial-Expert (MoGE) Transformer, to effectively integrate the embedding of multi-source geospatial data. Building on this, ReFound is enhanced by jointly distilling knowledge from language, visual, and visual-language FMs respectively, thus augmenting its generalization capabilities. Meanwhile, we design a masked geospatial data modeling approach alongside a cross-modal spatial alignment mechanism, to enhance the spatial knowledge of ReFound derived from geospatial data. Extensive experiments conducted on six real-world datasets over three urban region understanding tasks demonstrate the superior performance of our framework.
Minimum Description Length Based 2D Shape Description
Authors: Mengxiang Li
Venue: Conference ICCV 1993
Year: 1993

The problem of 2-D shape description, particularly with contour partitioning, grouping, and classification in terms of straight and curved, based on the minimum description length (MDL) criterion and shape-fitting techniques, is discussed. The MDL criterion is used to detect outliers in connection with shape fitting. Using the MDL criterion, it is possible to derive for a given data set and a class of models a description which best explains the data. A new algorithm for fitting 2-D points to an ellipse is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Exact Euclidean distance function by chain propagations
Authors: Luc Vincent
Venue: Conference CVPR 1991
Year: 1991

Up to now, all the known Euclidean distance function algorithms are either excessively slow or inaccurate, and even Danielsson's method (1980) produces errors in some configurations. The author shows that these problems are due to the local way distances are propagated in images by this algorithm. To remedy these drawbacks, an algorithm which encodes the objects boundaries as chains and propagates these structures in the image using rewriting rules is introduced. The chains convey Euclidean distances and can be written above one another, thus yielding exact results. In addition, the proposed algorithm is particularly efficient. Some of its applications to skeletons and neighborhood graphs are described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
A line-structure-preserving approach to image resizing
Authors: Che-Han Chang, Yung-Yu Chuang
Venue: Conference CVPR 2012
Year: 2012

This paper proposes a content-aware image resizing method which simultaneously preserves both salient image features and important line structure properties: parallelism, collinearity and orientation. When there are prominent line structures in the image, image resizing methods without explicitly taking these properties into account could produce line structure distortions in their results. Since the human visual system is very sensitive to line structures, such distortions often become noticeable and disturbing. Our method couples mesh deformations for image resizing with similarity transforms for line features. Mesh deformations are used to control content preservation while similarity transforms are analyzed in the Hough space to maintain line structure properties. Our method strikes a good balance between preserving content and maintaining line structure properties. Experiments show the proposed method often outperforms methods without taking line structures into account, especially for scenes with prominent line structures.
A Similarity-Based Probability Model for Latent Semantic Indexing
Authors: Chris H. Q. Ding
Venue: Conference SIGIR 1999
Year: 1999

A dual probability model is constructed for the Latent Semantic Indexing LSI using the cosine similarity measure. Both the document-document similarity matrix and the term-term similarity matrix naturally arise from the maximum likelihood estimation of the model parameters, and the optimal solutions are the latent semantic vectors of of LSI. Dimensionality reduction is justi ed by the statistical signi cance of latent semantic vectors as measured by the likelihood of the model. This leads to a statistical criterion for the optimal semantic dimensions, answering a critical open question in LSI with practical importance. Thus the model establishes a statistical framework for LSI. Ambiguities related to statistical modeling of LSI are clari ed.