Accurate link prediction method based on path length between a pair of unlinked nodes and their degree

Ayoub, Jibouni; Lotfi, Dounia; Marraki, Mohamed El; Hammouch, Ahmed

doi:10.1007/s13278-019-0618-2

Cited by 35 publications

(10 citation statements)

References 25 publications

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“…The flow of the Bitcoins is naturally subsumed in our analysis. The intuition of flow mergers being used as an evidence of relationship may be interpreted as a dual to the social network mining task of link prediction [20,4]. We illustrate the above algorithm by studying the flows branching out of 3HXdb.…”

Section: Algorithmmentioning

confidence: 99%

“…To gather data regarding different blackmail campaigns, we wrote a small Python tool 4 which first crawls relevant webpages based on keyword searches using (combinations of) words such as "Bitcoin", "scam", "blackmail", "secret", "webcam" and distinctive phrases (e.g. 'is fair price for our little secret') that we noted from the samples of sextortion emails, and then scrape the content of each such a page and extract the found Bitcoin addresses.…”

Section: One Scam Multiple Manifests?mentioning

confidence: 99%

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An ego network analysis of sextortionists

Oggier

Datta

Phetsouvanh

2020

Soc. Netw. Anal. Min.

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We consider a particular instance of user interactions in the Bitcoin network, that of interactions among wallet addresses belonging to scammers. Aggregation of multiple inputs and change addresses are common heuristics used to establish relationships among addresses and analyze transaction amounts in the Bitcoin network. We propose a flow centric approach that complements such heuristics, by studying the branching, merger and propagation of Bitcoin flows. We study a recent sextortion campaign by exploring the ego network of known offending wallet addresses. We compare and combine different existing and new heuristics, which allows us to identify (1) Bitcoin addresses of interest (including possible recurrent go-to addresses for the scammers) and ( 2) relevant Bitcoin flows, from scam Bitcoin addresses to a Binance exchange and to other other scam addresses, that suggest connections among prima facie disparate waves of similar scams.

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Section: Algorithmmentioning

confidence: 99%

Section: One Scam Multiple Manifests?mentioning

confidence: 99%

An ego network analysis of sextortionists

Oggier

Datta

Phetsouvanh

2020

Soc. Netw. Anal. Min.

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“…Then, the penalization parameter for a network is predicted using logistic regression. In [10,11], we proposed a similarity metric based on the path depth from a source node to a destination node and their degrees. In addition, we found a strong correlation between the clustering coefficient and area under the curve.…”

Section: Introductionmentioning

confidence: 99%

Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

2022

Self Cite

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The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

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“…Therefore, predicting these links is still challenging and it is necessary to achieve a predictive method with acceptable precision. Although, the link prediction problem has been extensively studied and various researches have been presented to solve it [7][8][9]; However, the problem of how to optimally and effectively combine information to describe future communications remains largely unresolved. In [10], to link prediction in social networks, the analysis of user's demographic features has been used.…”

Section: Introductionmentioning

confidence: 99%

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Yang

Peng

et al. 2022

Concurrency and Computation

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Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user' on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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Accurate link prediction method based on path length between a pair of unlinked nodes and their degree

Cited by 35 publications

References 25 publications

An ego network analysis of sextortionists

An ego network analysis of sextortionists

Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

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