Edge Weight Prediction in Weighted Signed Networks

Kumar, Srijan; Spezzano, Francesca; Subrahmanian, V. S.; Faloutsos, Christos

doi:10.1109/icdm.2016.0033

Cited by 366 publications

(240 citation statements)

References 32 publications

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“…In the definition of edge weight prediction on WSNs by Kumar et al [KSSF16], they adapted several algorithms for this task, providing us with a set of baseline measures on these datasets. We first use a basic method of Reciprocal [KSSF16], where the edge weight w u,v is same as that of the reciprocal edge weight w v,u if there exists an edge e + v, u ∈ E, and 0 otherwise. We then use two graph algorithms PageRank [PBMW98] and Signed Eigenvector Centrality [Bon07].…”

Section: Resultsmentioning

confidence: 99%

Learning Edge Properties in Graphs from Path Aggregations

Agrawal

Alfaro

2019

The World Wide Web Conference

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Graph edges, along with their labels, can represent information of fundamental importance, such as links between web pages, friendship between users, the rating given by users to other users or items, and much more. We introduce LEAP, a trainable, general framework for predicting the presence and properties of edges on the basis of the local structure, topology, and labels of the graph. The LEAP framework is based on the exploration and machine-learning aggregation of the paths connecting nodes in a graph. We provide several methods for performing the aggregation phase by training path aggregators, and we demonstrate the flexibility and generality of the framework by applying it to the prediction of links and user ratings in social networks.We validate the LEAP framework on two problems: link prediction, and user rating prediction. On eight large datasets, among which the arXiv collaboration network, the Yeast protein-protein interaction, and the US airlines routes network, we show that the link prediction performance of LEAP is at least as good as the current state of the art methods, such as SEAL and WLNM. Next, we consider the problem of predicting user ratings on other users: this problem is known as the edge-weight prediction problem in weighted signed networks (WSN). On Bitcoin networks, and Wikipedia RfA, we show that LEAP performs consistently better than the Fairness & Goodness based regression models, varying the amount of training edges between 10 to 90%. These examples demonstrate that LEAP, in spite of its generality, can match or best the performance of approaches that have been especially crafted to solve very specific edge prediction problems.

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Section: Resultsmentioning

confidence: 99%

Learning Edge Properties in Graphs from Path Aggregations

Agrawal

Alfaro

2019

The World Wide Web Conference

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“…Fourth, Bitcoin OTC is an over-the-counter marketplace where users trade with bitcoin [36]. In Bitcoin OTC, users rate other users regarding the trustworthiness.…”

Section: Datamentioning

confidence: 99%

Long-tailed distributions of inter-event times as mixtures of exponential distributions

2020

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Inter-event times of various human behavior are apparently non-Poissonian and obey long-tailed distributions as opposed to exponential distributions, which correspond to Poisson processes. It has been suggested that human individuals may switch between different states in each of which they are regarded to generate events obeying a Poisson process. If this is the case, distributions of inter-event times should approximately obey a mixture of exponential distributions with different parameter values. In the present study, we introduce the minimum description length principle to compare mixtures of exponential distributions with different numbers of components (i.e., constituent exponential distributions). Because these distributions violate the identifiability property, one is mathematically not allowed to apply the Akaike or Bayes information criteria to their maximum likelihood estimator to carry out model selection. We overcome this theoretical barrier by applying a minimum description principle to joint likelihoods of the data and latent variables. We show that mixtures of exponential distributions with a few components are selected as opposed to more complex mixtures in various data sets and that the fitting accuracy is comparable to that of state-of-the-art algorithms to fit powerlaw distributions to data. Our results lend support to Poissonian explanations of apparently non-Poissonian human behavior.1

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“…We provide more details of the datasets in Table 2. The Bitcoin-Alpha and Bitcoin-OTC datasets were collected from Bitcoin Alpha 1 and Bitcoin OTC 2 , respectively [13]. In these sites, users can buy and sell things in an open marketplace using Bitcoins.…”

Section: Datasetsmentioning

confidence: 99%

Signed Network Modeling Based on Structural Balance Theory

Derr

Aggarwal

Tang

2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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The modeling of networks, specifically generative models, has been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. There has been a considerable increase in interest for the better understanding and modeling of networks, and the vast majority of existing work has been for unsigned networks. However, many networks can have positive and negative links (or signed networks), especially in online social media. It is evident from recent work that signed networks present unique properties and principles from unsigned networks due to the added complexity, which pose tremendous challenges on existing unsigned network models. Hence, in this paper, we investigate the problem of modeling signed networks. In particular, we provide a principled approach to capture important properties and principles of signed networks and propose a novel signed network model guided by Structural Balance Theory. Empirical experiments on three real-world signed networks demonstrate the effectiveness of the proposed model.

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Edge Weight Prediction in Weighted Signed Networks

Cited by 366 publications

References 32 publications

Learning Edge Properties in Graphs from Path Aggregations

Learning Edge Properties in Graphs from Path Aggregations

Long-tailed distributions of inter-event times as mixtures of exponential distributions

Signed Network Modeling Based on Structural Balance Theory

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