A Random Walk based Trust Ranking in Distributed Systems

Honest cooperation among individuals in a network can be achieved in different ways. In online networks with some kind of central authority, such as Ebay, Airbnb, etc. honesty is achieved through a reputation system, which is maintained and secured by the central authority. These systems usually rely on review mechanisms, through which agents can evaluate the trustworthiness of their interaction partners. These reviews are stored centrally and are tamper-proof. In decentralized peer-to-peer networks, enforcing cooperation turns out to be more difficult. One way of approaching this problem is by observing cooperative biological communities in nature. One finds that cooperation among biological organisms is achieved through a mechanism called indirect reciprocity. This mechanism for cooperation relies on some shared notion of trust. In this work we aim to facilitate communal cooperation in a peer-to-peer file sharing network called Tribler, by introducing a mechanism for evaluating the trustworthiness of agents. We determine a trust ranking of all nodes in the network based on the Monte Carlo algorithm estimating the values of Google's personalized PageRank vector. We go on to evaluate the algorithm's resistance to Sybil attacks, whereby our aim is for sybils to be assigned low trust scores.Comment: 13 pages, 15 figure

SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and being not scalable. In this work, we aim to overcome these drawbacks. Towards this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.Comment: 12 page

Recommender Systems with Random Walks: A Survey

Recommender engines have become an integral component in today's e-commerce systems. From recommending books in Amazon to finding friends in social networks such as Facebook, they have become omnipresent. Generally, recommender systems can be classified into two main categories: content based and collaborative filtering based models. Both these models build relationships between users and items to provide recommendations. Content based systems achieve this task by utilizing features extracted from the context available, whereas collaborative systems use shared interests between user-item subsets. There is another relatively unexplored approach for providing recommendations that utilizes a stochastic process named random walks. This study is a survey exploring use cases of random walks in recommender systems and an attempt at classifying them.Comment: 15 pages, a survey pape

Black Hole Metric: Overcoming the PageRank Normalization Problem

In network science, there is often the need to sort the graph nodes. While the sorting strategy may be different, in general sorting is performed by exploiting the network structure. In particular, the metric PageRank has been used in the past decade in different ways to produce a ranking based on how many neighbors point to a specific node. PageRank is simple, easy to compute and effective in many applications, however it comes with a price: as PageRank is an application of the random walker, the arc weights need to be normalized. This normalization, while necessary, introduces a series of unwanted side-effects. In this paper, we propose a generalization of PageRank named Black Hole Metric which mitigates the problem. We devise a scenario in which the side-effects are particularily impactful on the ranking, test the new metric in both real and synthetic networks, and show the results.Comment: 21 pages, 7 figure

A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems

Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many e-commerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. However, it is still unclear which method provides better recommendation performance despite their extensive utility. In this paper, we provide a comparative study of matrix factorization and RWR in recommender systems. We exactly formulate each correspondence of the two methods according to various tasks in recommendation. Especially, we newly devise an RWR method using global bias term which corresponds to a matrix factorization method using biases. We describe details of the two methods in various aspects of recommendation quality such as how those methods handle cold-start problem which typically happens in collaborative filtering. We extensively perform experiments over real-world datasets to evaluate the performance of each method in terms of various measures. We observe that matrix factorization performs better with explicit feedback ratings while RWR is better with implicit ones. We also observe that exploiting global popularities of items is advantageous in the performance and that side information produces positive synergy with explicit feedback but gives negative effects with implicit one.Comment: 10 pages, Appears in IEEE International Conference on Big Data 2017 (IEEE BigData 2017

Systems Applications of Social Networks

The aim of this article is to provide an understanding of social networks as a useful addition to the standard tool-box of techniques used by system designers. To this end, we give examples of how data about social links have been collected and used in di erent application contexts. We develop a broad taxonomy-based overview of common properties of social networks, review how they might be used in di erent applications, and point out potential pitfalls where appropriate. We propose a framework, distinguishing between two main types of social network-based user selection-personalised user selection which identi es target users who may be relevant for a given source node, using the social network around the source as a context, and generic user selection or group delimitation, which lters for a set of users who satisfy a set of application requirements based on their social properties. Using this framework, we survey applications of social networks in three typical kinds of application scenarios: recommender systems, content-sharing systems (e.g., P2P or video streaming), and systems which defend against users who abuse the system (e.g., spam or sybil attacks). In each case, we discuss potential directions for future research that involve using social network properties.Comment: Will appear in ACM computing Survey

Network-based information filtering algorithms: ranking and recommendation

After the Internet and the World Wide Web have become popular and widely-available, the electronically stored online interactions of individuals have fast emerged as a challenge for researchers and, perhaps even faster, as a source of valuable information for entrepreneurs. We now have detailed records of informal friendship relations in social networks, purchases on e-commerce sites, various sorts of information being sent from one user to another, online collections of web bookmarks, and many other data sets that allow us to pose questions that are of interest from both academical and commercial point of view. For example, which other users of a social network you might want to be friend with? Which other items you might be interested to purchase? Who are the most influential users in a network? Which web page you might want to visit next? All these questions are not only interesting per se but the answers to them may help entrepreneurs provide better service to their customers and, ultimately, increase their profits.Comment: book chapter; 21 pages, 5 figures, 1 tabl

The Art of Social Bots: A Review and a Refined Taxonomy

Social bots represent a new generation of bots that make use of online social networks (OSNs) as a command and control (C\&C) channel. Malicious social bots were responsible for launching large-scale spam campaigns, promoting low-cap stocks, manipulating user's digital influence and conducting political astroturf. This paper presents a detailed review on current social bots and proper techniques that can be used to fly under the radar of OSNs defences to be undetectable for long periods of time. We also suggest a refined taxonomy of detection approaches from social network perspective, as well as commonly used datasets and their corresponding findings. Our study can help OSN administrators and researchers understand the destructive potential of malicious social bots and can improve the current defence strategies against them

Preserving Local and Global Information for Network Embedding

Networks such as social networks, airplane networks, and citation networks are ubiquitous. The adjacency matrix is often adopted to represent a network, which is usually high dimensional and sparse. However, to apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while completely neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. Hence, it also has potential to advance network embedding. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model {\bf LO}cal and {\bf G}lobal information. Experimental results demonstrate the ability to preserve global information of the proposed framework. Further experiments are conducted to demonstrate the effectiveness of learned representations of the proposed framework

IteRank: An iterative network-oriented approach to neighbor-based collaborative ranking

Neighbor-based collaborative ranking (NCR) techniques follow three consecutive steps to recommend items to each target user: first they calculate the similarities among users, then they estimate concordance of pairwise preferences to the target user based on the calculated similarities. Finally, they use estimated pairwise preferences to infer the total ranking of items for the target user. This general approach faces some problems as the rank data is usually sparse as users usually have compared only a few pairs of items and consequently, the similarities among users is calculated based on limited information and is not accurate enough for inferring true values of preference concordance and can lead to an invalid ranking of items. This article presents a novel framework, called IteRank, that models the data as a bipartite network containing users and pairwise preferences. It then simultaneously refines users' similarities and preferences' concordances using a random walk method on this graph structure. It uses the information in this first step in another network structure for simultaneously adjusting the concordances of preferences and rankings of items. Using this approach, IteRank can overcome some existing problems caused by the sparsity of the data. Experimental results show that IteRank improves the performance of recommendation compared to the state of the art NCR techniques that use the traditional NCR framework for recommendation