1,445 research outputs found
Graph based Anomaly Detection and Description: A Survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the âwhyâ, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field
Enhancing explainability and scrutability of recommender systems
Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithmâs behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in ďŹltering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the systemâs behavior can be modiďŹed accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: ⢠We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between usersâ proďŹles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. ⢠We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the userâs prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for ďŹnding the smallest counterfactual explanations. ⢠We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-speciďŹc item representations. We evaluate all proposed models and methods with real user studies and demonstrate their beneďŹts at achieving explainability and scrutability in recommender systems.Unsere zunehmende Abhängigkeit von komplexen Algorithmen fĂźr maschinelle Empfehlungen erfordert Modelle und Methoden fĂźr erklärbare, nachvollziehbare und vertrauenswĂźrdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklärbar sein. MĂśchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen ändern, muss dessen Entscheidungsprozess nachvollziehbar sein. Erklärbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die LĂźcke zwischen dem von uns erwarteten und dem tatsächlichen Verhalten der Algorithmen zu schlieĂen und unser Vertrauen in KI-Systeme entsprechend zu stärken. Um ein ĂbermaĂ an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu ďŹltern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene Erklärungen fĂźr deren personalisierte Empfehlungen. Diese Erklärungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre frĂźheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. AuĂerdem kĂśnnen Erklärungen fĂźr den Fall, dass unerwĂźnschte Inhalte empfohlen werden, wertvolle Informationen darĂźber enthalten, wie das Verhalten des Systems entsprechend geändert werden kann. In dieser Dissertation stellen wir unsere Beiträge zu Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. ⢠Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc Erklärungen fĂźr die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden kĂśnnen. Diese Erklärungen zeigen Beziehungen zwischen BenutzerproďŹlen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die Erklärungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. ⢠Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von Erklärungen fĂźr PageRank-basierte Empfehlungsdienste. PRINCE-Erklärungen sind fĂźr Benutzer verständlich, da sie Teilmengen frĂźherer Nutzerinteraktionen darstellen, die fĂźr die erhaltenen Empfehlungen verantwortlich sind. PRINCE-Erklärungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um präzise Erklärungen zu ďŹnden. ⢠Wir präsentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die Qualität der Empfehlungen zu verbessern. Mit ELIXIR kĂśnnen Empfehlungsdienste Benutzerfeedback zu Empfehlungen und Erklärungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspeziďŹscher Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten
MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network
Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased âThreatâ of an âAttackâ. Attackers are targeting these devices, as it may provide an easier âbackdoor entry to the usersâ networkâ.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method
Integrated smoothed location model and data reduction approaches for multi variables classification
Smoothed Location Model is a classification rule that deals with mixture of continuous variables and binary variables simultaneously. This rule discriminates groups in a parametric form using conditional distribution of the continuous variables given each pattern of the binary variables. To conduct a practical
classification analysis, the objects must first be sorted into the cells of a multinomial table generated from the binary variables. Then, the parameters in each cell will be estimated using the sorted objects. However, in many situations, the estimated parameters are poor if the number of binary is large relative to the size of sample. Large binary variables will create too many multinomial cells which are empty, leading to high sparsity problem and finally give exceedingly poor performance for
the constructed rule. In the worst case scenario, the rule cannot be constructed. To
overcome such shortcomings, this study proposes new strategies to extract adequate variables that contribute to optimum performance of the rule. Combinations of two extraction techniques are introduced, namely 2PCA and PCA+MCA with new cutpoints of eigenvalue and total variance explained, to determine adequate extracted
variables which lead to minimum misclassification rate. The outcomes from these
extraction techniques are used to construct the smoothed location models, which then produce two new approaches of classification called 2PCALM and 2DLM. Numerical evidence from simulation studies demonstrates that the computed misclassification rate indicates no significant difference between the extraction
techniques in normal and non-normal data. Nevertheless, both proposed approaches are slightly affected for non-normal data and severely affected for highly overlapping groups. Investigations on some real data sets show that the two approaches are competitive with, and better than other existing classification methods. The overall findings reveal that both proposed approaches can be
considered as improvement to the location model, and alternatives to other classification methods particularly in handling mixed variables with large binary size
Named Entity Resolution in Personal Knowledge Graphs
Entity Resolution (ER) is the problem of determining when two entities refer
to the same underlying entity. The problem has been studied for over 50 years,
and most recently, has taken on new importance in an era of large,
heterogeneous 'knowledge graphs' published on the Web and used widely in
domains as wide ranging as social media, e-commerce and search. This chapter
will discuss the specific problem of named ER in the context of personal
knowledge graphs (PKGs). We begin with a formal definition of the problem, and
the components necessary for doing high-quality and efficient ER. We also
discuss some challenges that are expected to arise for Web-scale data. Next, we
provide a brief literature review, with a special focus on how existing
techniques can potentially apply to PKGs. We conclude the chapter by covering
some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct.
2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and
applications' edited by Tiwari et a
FairGen: Towards Fair Graph Generation
There have been tremendous efforts over the past decades dedicated to the
generation of realistic graphs in a variety of domains, ranging from social
networks to computer networks, from gene regulatory networks to online
transaction networks. Despite the remarkable success, the vast majority of
these works are unsupervised in nature and are typically trained to minimize
the expected graph reconstruction loss, which would result in the
representation disparity issue in the generated graphs, i.e., the protected
groups (often minorities) contribute less to the objective and thus suffer from
systematically higher errors. In this paper, we aim to tailor graph generation
to downstream mining tasks by leveraging label information and user-preferred
parity constraint. In particular, we start from the investigation of
representation disparity in the context of graph generative models. To mitigate
the disparity, we propose a fairness-aware graph generative model named
FairGen. Our model jointly trains a label-informed graph generation module and
a fair representation learning module by progressively learning the behaviors
of the protected and unprotected groups, from the `easy' concepts to the `hard'
ones. In addition, we propose a generic context sampling strategy for graph
generative models, which is proven to be capable of fairly capturing the
contextual information of each group with a high probability. Experimental
results on seven real-world data sets, including web-based graphs, demonstrate
that FairGen (1) obtains performance on par with state-of-the-art graph
generative models across six network properties, (2) mitigates the
representation disparity issues in the generated graphs, and (3) substantially
boosts the model performance by up to 17% in downstream tasks via data
augmentation
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