Voting Networks Research Articles

Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data

Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.

A novel link prediction method integrated link attributes for directed graph

Link prediction is an important topic in the complex networks which aims to mine the missed or unobserved links and find the possible connected links in the network according to the known network structure. In this paper, we proposed a novel link prediction method for the directed graph which considers link semantic. The experimental results on the wiki vote network show that our link prediction method integrating link attributes achieved better results than the other compared methods.

An Efficient Open Vote Network for Multiple Candidates

In 2010, Hao et al. proposed an efficient decentralized voting protocol known as Open Vote Network (OV-Net), which does not require a trusted third party to count the votes or a private channel to protect ballot secrecy. In the last decade, various studies have been conducted to solve some limitations of the OV-Net, such as fairness and robustness. However, an unresolved problem exists in the OV-Net (and its variants), which is its limited scalability for multiple candidates. The computational cost for tallying, which increases exponentially with the number of candidates, causes the scalability problem. Therefore, in this study, we solve this problem by proposing a variant of the OV-Net for multiple candidates and showing that the computational cost of tallying increases linearly with the number of candidates. Regarding security, we prove that our variant satisfies the ballot secrecy and dispute-freeness in formal security models, based on the decision Diffie-Hellman assumption and non-interactive zero-knowledge properties, respectively. Moreover, regarding the efficiency, we compare the performance of the traditional and proposed OV-Net from the theoretical standpoint and present experimental results to show that the proposed OV-Net variant is considerably more efficient than the traditional OV-Net as the numbers of voters and candidates increase.

Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling

For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is hard, due to the heterogeneous nature of the disease. We devised a methodology for predicting 5-years survival based on the new machine learning paradigm of coherent voting networks, with improved accuracy over state-of-the-art prediction methods. The ’coherent voting communities’ metaphor provides a certificate justifying the survival prediction for an individual patient, thus facilitating its acceptability in practice, in the vein of explainable Artificial Intelligence. The method we propose is quite flexible and applicable to other types of cancer.

The evaluation of voting rules based on voting networks: Weber problem with support domain

This study focuses on improving the Weber model to obtain a more optimised solution, by which we can measure effectively the quality of the voting rules. By introducing the voter’s supporting domain and the candidate’s supported degree to depict the emotional factors of voters and the real voting network, we propose the concept of validity for a candidate. Upon the traditional Weber model, two improved models are presented and the corresponding global optimal candidate is used as the evaluation benchmark for the voting rules. The experiments show that the optimal solution of the two models has better robustness in complex voting networks, can be used as a standard to evaluate the voting rules. When the support degree of voter is the main factor, the Condorcet rule is optimal in most cases, and when the validity of the candidate is taken as the main factor, the Approval rule must be the best.

FORMAN–RICCI CURVATURE FOR HYPERGRAPHS

Hypergraphs serve as models of complex networks that capture more general structures than binary relations. For graphs, a wide array of statistics has been devised to gauge different aspects of their structures. Hypergraphs lack behind in this respect. The Forman–Ricci curvature is a statistics for graphs based on Riemannian geometry, which stresses the relational character of vertices in a network by focusing on the edges rather than on the vertices. Despite many successful applications of this measure to graphs, Forman–Ricci curvature has not been introduced for hypergraphs. Here, we define the Forman–Ricci curvature for directed and undirected hypergraphs such that the curvature for graphs is recovered as a special case. It quantifies the trade-off between hyperedge (arc) size and the degree of participation of hyperedge (arc) vertices in other hyperedges (arcs). Here, we determine upper and lower bounds for Forman–Ricci curvature both for hypergraphs in general and for graphs in particular. The measure is then applied to two large networks: the Wikipedia vote network and the metabolic network of the bacterium Escherichia coli. In the first case, the curvature is governed by the size of the hyperedges, while in the second example, it is dominated by the hyperedge degree. We found that the number of users involved in Wikipedia elections goes hand-in-hand with the participation of experienced users. The curvature values of the metabolic network allowed detecting redundant and bottle neck reactions. It is found that ADP phosphorylation is the metabolic bottle neck reaction but that the reverse reaction is not similarly central for the metabolism. Furthermore, we show the utility of the Forman–Ricci curvature for quantification of assortativity in hypergraphs and illustrate the idea by investigating three metabolic networks.

It is not just about the melody: How Europe votes for its favourite songs

The Eurovision Song Contest is a popular annual international song competition organized by the European Broadcasting Union. The winner is decided by the audience and expert juries from each participating nation, which is why the analysis of its voting network offers a great insight into what factors, beside the quality of the performances, influence the voting decisions.
 In this paper, we present the findings of the analysis of the voting network together with the results of a predictive model based on the collected data. We touch upon the methodology used and describe the dataset that we are analyzing. The results include a number of general features of the voting networks, the exposed communities of countries that award significantly more points to each other than would be expected and predictions on what the biggest factors that lead to this phenomenon are. Multiple known favourable relationships are exposed, such as the one between Greece and Cyprus, between Scandinavian countries and between the countries of the former Yugoslavia. A general trend of neighbouring countries forming alliances is observed, which is especially apparent if they are culturally or geographically separated from others. Furthermore, it is also observed how membership in communities of common point exchange helps achieve better results in the competition and that this trend is on the rise. At the same time, countries involved in relationships of neglect often achieve poorer results. We also try out a model in order to predict the votes based on the network structure of both previous votes and song preferences of nations, which was found not to offer significant improvement of predictions compared to betting tables alone.

How Brazilian congressmen connect: homophily and cohesion in voting and donation networks

Abstract Two fundamental activities in the career of a congressman are raising campaign funds and voting on bills. Characterizing the interplay between these two activities is crucial for democracy as it provides explicit feedback to voters on potential conflicts of interest. In this work, we investigate the Brazilian Congress to shed light on the relationships between the donations received by congressmen elected in 2014 and their voting behaviours during 2015 and 2016. By consolidating publicly available data from the House of Representatives and the Superior Electoral Court (TSE), we construct a dataset containing elected congressmen, campaign donors and donations, and legal bills and votes. This leads to the donation network (edges represent co-donation) and voting network (edges represent co-voting) in which nodes are congressmen. We examine homophily and cohesion of congressmen in these two networks with respect to their political parties and electoral regions. Our results indicate that regions exhibit stronger homophily than political parties in the donation network, while this trend is opposite for the voting network. In this sense, we find that partisan cohesion is strong and diverse in the voting network, while regional cohesion is consistently negligible. These findings are an indication of Brazil’s fragmented and weak federalist system where partisan interests override regional interests.

Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks

BackgroundIdentifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are developed in various small datasets, which each represent a limited sample space. Thus, the prediction accuracy of these tools has not been systematically validated at a larger scale. Accordingly, comparing the prediction accuracy of these tools and determining their applicability become challenging. In addition, the accuracy of these tools, especially in large datasets, needs to be improved for broader applications.ResultsIn this project, a large dataset containing more than 46,600 miRNA:mRNA interactions was assembled and split into eleven subsets based on the availability of prediction scores of four individual predictors, which are miRanda, miRDB, PITA, and TargetScan. In each of these subsets, the predictive results of four individual predictors were integrated using decision-tree based artificial neural networks to make the meta-prediction. The decision-tree is used here to sort the predictive results of four individual predictors, and artificial neural networks are applied to make meta-prediction based on the outputs of individual predictors. In the decision tree, dual-threshold and two-step significance-voting were incorporated, information gain was analysed to select threshold values. The prediction performance of this new strategy was improved significantly in most of the eleven datasets comparing to the individual predictors and other meta-predictors, such as ComiR, under multi-fold cross-validation, as well as in independent datasets. The overall improvement of prediction accuracy in independent datasets is at least 9 percentile points comparing to the other predictors, and the percentage of improvement of F1 and MCC scores is at least 40% compared to the other predictors.ConclusionsThe combination of dual-threshold, two-step significance-voting, and analysis of information gain is very effective in optimizing the outcome of decision-tree, and further integration with artificial neural networks is critical for further improving the performance of meta-predictor. A new pipeline based on this integration for miRNA target prediction has been developed. A strategy using outputs of individual predictors to reorganize large-scale miRNA:mRNA interaction dataset has also been validated and used to evaluate the prediction accuracy of predictors. The predictor is available at: https://github.com/xueLab/mirTarDANN).

The collapse of the second Yatsenyuk government: roll call vote and dynamic network analysis

This paper investigates the cause of the collapse of the Second Yatsenyuk Ukrainian government in 2016 and the factions formed throughout the process using a novel method that combines roll call vote analysis and dynamic network analysis. Our findings suggest that a major factor in the dissolution of the coalition government was the 2015 constitutional amendment .We also found that the coalition split into two factions following this event: the People’s Front and Petro Poroshenko Bloc as one faction, and the Union and Fatherland as the other. This split could have a large impact on the 2019 Ukrainian Election and give current President Petro Poroshenko an advantage over Yulia Tymoshenko, who is leading in the polls.

Perseus

Given a large graph with several millions or billions of nodes and edges, such as a social network, how can we explore it efficiently and find out what is in the data? In this demo we present P erseus , a large-scale system that enables the comprehensive analysis of large graphs by supporting the coupled summarization of graph properties and structures, guiding attention to outliers, and allowing the user to interactively explore normal and anomalous node behaviors. Specifically, P erseus provides for the following operations: 1) It automatically extracts graph invariants ( e.g. , degree, PageRank, real eigenvectors) by performing scalable, offline batch processing on H adoop ; 2) It interactively visualizes univariate and bivariate distributions for those invariants; 3) It summarizes the properties of the nodes that the user selects; 4) It efficiently visualizes the induced subgraph of a selected node and its neighbors, by incrementally revealing its neighbors. In our demonstration, we invite the audience to interact with P erseus to explore a variety of multi-million-edge social networks including a Wikipedia vote network, a friendship/foeship network in Slashdot, and a trust network based on the consumer review website Epinions.com.

Split Ticket Voting and Social Networks in New and Established Democracies: A Quasi-Experiment in the 1990 German Unification Elections

When deciding to cast a split ticket ballot, voters in established democracies can rely on their experience with democracy and long term party attachments to guide their voting behavior. However, voters in new democracies do not have these individual level sources of information and motivation, and may instead rely on information from their social networks. Using the 1990 Cross-National Election Project German Unification election study, I examine the information sources West and East Germans use when splitting their ballot. I find that political disagreement within a social network is much more influential in decisions to cast a split ticket ballot for East German voters, while individual-level traits, particularly party attachment, play a greater role for West German voters. These findings indicate that, in absence of competition between individual-level information sources, network characteristics may have a profound impact on political decision-making.

Efficient hyperlink analysis using robust Proportionate Prestige Score in PageRank algorithm

Existing PageRank algorithm exploits the Hyperlink Structure of the web with uniform transition probability distribution to measure the relative importance of web pages. This paper proposes a novel method namely Proportionate Prestige Score (PPS) for prestige analysis. This proposed PPS method is purely based on the exact prestige of web pages, which is applicable to Initial Probability Distribution (IPD) matrix and Transition Probability Distribution (TPD) matrix. This proposed PPS method computes the single PageRank vector with non-uniform transition probability distribution, using the link structure of the web pages offline. This non-uniform transition probability distribution has efficiently overcome the dangling page problem than the existing PageRank algorithm. This paper provides benchmark analysis of ranking methods: PageRank and proposed PPS. These methods are tested with real social network data from three different domains: Social Circle:Facebook, Wikipedia vote network and Enron email network. The findings of this research work propose that the quality of the ranking has improved by using the proposed PPS method compared with the existing PageRank algorithm.

Analysis and performance of network decoding strategies for cooperative network coding

In this work, we present two new low-complexity network decoding strategies for cooperative network coding in a multiple-access relay channel scenario. For these two strategies, Selection and Soft Combining and Majority Vote Network Decoding, along with the optimal joint network decoding, we derive expressions for bit error probability performance as a function of the signal-to-noise ratio (SNR) of the different Rayleigh fading links, and show the tightness of the derived bounds through simulation results. The two proposed schemes provide a similar bit error probability (BEP) performance compared to the optimal scheme, while having significantly lower complexity. Further, we study the effect of user pairing on the error performance by considering different SNRs on the user and relay links towards the destination. It is also shown that the error performance of the different schemes follows the same trend for a given user pairing strategy.

Communities in multislice voting networks

Communities in multislice voting networks

Randomness in generalization ability: a source to improve it

Among several models of neurons and their interconnections, feedforward artificial neural networks (FFANNs) are most popular, because of their simplicity and effectiveness. Difficulties such as long learning time and local minima may not affect FFANNs as much as the question of generalization ability, because a network needs only one training, and then it may be used for a long time. This paper reports our observations about randomness in generalization ability of FFANNs. A novel method for measuring generalization ability is defined. This method can be used to identify degree of randomness in generalization ability of learning systems. If an FFANN architecture shows randomness in generalization ability for a given problem, multiple networks can be used to improve it. We have developed a model, called voting model, for predicting generalization ability of multiple networks. It has been shown that if correct classification probability of a single network is greater than half, then as the number of networks in a voting network is increased so does its generalization ability. Further analysis has shown that VC-dimension of the voting network model may increase monotonically as the number of networks in the voting networks is increased.