Influence Ranking Research Articles

Defining and evaluating Twitter influence metrics: a higher-order approach in Neo4j

Ranking account influence constitutes an important challenge in social media analysis. Until recently, influence ranking relied solely on the structural properties of the underlying social graph, in particular on connectivity patterns. Currently, there has been a notable shift to the next logical step where network functionality is taken into account, as online social media such as Reddit, Instagram, and Twitter are renowned primarily for their functionality. However, contrary to structural rankings, functional ones are bound to be network-specific since each social platform offers unique interaction possibilities. This article examines seven first-order influence metrics for Twitter, defines a strategy for deriving their higher-order counterparts, and outlines a probabilistic evaluation framework. Experiments with a Twitter subgraph with ground truth influential accounts indicate that a single metric combining structural and functional features outperforms the rest in said framework.

Spatial variation in diurnal courses of stem temperature of Betula platyphylla and Fraxinus mandshurica and its influencing factors

Plant temperature is an important parameter for estimating energy balance and vegetation respiration of forest ecosystem. To examine spatial variation in diurnal courses of stem temperatures (Ts) and its influencing factors, we measured the Ts with copper constantan thermocouples at different depths, heights and azimuths within the stems of two broadleaved tree species with contrasting bark and wood properties, Betula platyphylla and Fraxinus mandshurica. The results showed that the monthly mean diurnal courses of the Ts largely followed that of air temperature with a 'sinusoi dal' pattern, but the Ts lagged behind the air temperature by 0 h at the stem surface to 4 h at 6 cm depth. The daily maximal values and ranges of the diurnal course of Ts decreased gradually with increasing measuring depth across the stem and decreasing measuring height along the stem. The circumferential variation in Ts was marginal, with slightly higher daily maximal values in the south and west directions during the daytime of the dormant season. Differences in thermal properties (i.e. , specific heat capacity and thermal conductivity) of both bark and wood tissue between the two species contributed to the inter specific variations in the radial variation in Ts through influencing the heat exchange between the stem surface and ambient air as well as heat diffusion within the stem. The higher reflectance of the bark of B. platyphylla decreased the influence of solar radiation on Ts. The stepwise regression showed that the diurnal courses of Ts could be well predicted by the environmental factors (R2 > 0.85) with an order of influence ranking as air temperature > water vapor pressure > net radiation > wind speed. It is necessary to take the radial, vertical and inter specific varia-tions in Ts into account when estimating biomass heat storage and stem CO2 efflux.

Study on Improved Influence Page Rank Algorithm Based On User Behavior

User influence ranking is an important subject of social network research, for public opinion control, important dissemination and so on is a great significance. The traditional PageRank algorithm only considers the relationship between users, this paper combines the Page Rank algorithm with the user behavior, integrates the follower quality, comment rate, forwarding rate and whether the authentication behavior and other user behavior factors, proposed an improved algorithm URank. Experiments based on the SIR propagation model show that the URank algorithm is superior to the PageRank algorithm in calculating the accuracy of the influence index for large-scale data sets.

Integrating Ego, Homophily, and Structural Factors to Measure User Influence in Online Community

Research problem: In the current information age, people are increasingly accustomed to sharing their special interests online and are influenced by the relationships developed from that sharing. The purpose of this study was to better measure peer influence in these online communities. Research questions: 1. How can peer influence in online communities be measured in a way that comprehensively incorporates peer-based characteristics, the homophily effect, and the structural position of a user in the network? 2. Is the method proposed in this study superior to other existing methods? Literature review: Previous literature on measuring online user influence can be classified into two streams: 1. Those that focus on the intrinsic characteristics of social media players to measure peer influence; 2. Those that address social network structure. Relevant computing algorithms include Topic-Based PageRank, Quality-Structure index, and so on. Although the first stream considers a focal peer's intrinsic characteristics, it overlooks the interpeer attraction in terms of similarity and discrepant knowledge among peers. The second stream mostly stresses the structures of social networks to measure network-wide peer influence but underestimates the effect of interpeer attraction that may leverage every diffusion step of peer influence through the network. To fill this research gap, this study proposes a new method of measuring network user influence that incorporates peers’ intrinsic factors, interpeer influence factors as homophily effect, and network structure. Homophily refers to the degree to which pairs of individuals who interact are similar with respect to certain attributes. Methodology: From the communication sender–receiver perspective, we developed a computable method that incorporates peer-based characteristics, the homophily effect, and the structural position of a user in the network to measure the social network user influence. Two empirical studies were subsequently conducted in a social network service-based online community and an online professional logistics community to verify the effectiveness of the proposed method. Results and conclusions: The empirical results show that our proposed method provides higher prediction accuracy of user influence rank in an online community than the other existing methods. These findings lay a foundation for future theoretical exploration and provide a useful tool for targeting influential users in online communities such as blogs, bulletin board systems, and forums.

A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas

Sensitivity analyses (SAs) identify how an output variable of a model is modified by changes in the input variables. These analyses are a good way for assessing the performance of probabilistic models, like Bayesian Networks (BN). However, there are several commonly used SAs in BN literature, and formal comparisons about their outcomes are scarce. We used four previously developed BNs which represent ecosystem services provision in Pampean agroecosystems (Argentina) in order to test two local sensitivity approaches widely used. These SAs were: 1) One-at-a-time, used in BNs but more commonly in linear modelling; and 2) Sensitivity to findings, specific to BN modelling. Results showed that both analyses provided an adequate overview of BN behaviour. Furthermore, analyses produced a similar influence ranking of input variables over each output variable. Even though their interchangeably application could be an alternative in our bayesian models, we believe that OAT is the suitable one to implement here because of its capacity to demonstrate the relation (positive or negative) between input and output variables. In summary, we provided insights about two sensitivity techniques in BNs based on a case study which may be useful for ecological modellers.

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.

Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks

Designing node influence ranking algorithms can provide insights into network dynamics, functions and structures. Increasingly evidences reveal that node’s spreading ability largely depends on its neighbours. We introduce an iterative neighbourinformation gathering (Ing) process with three parameters, including a transformation matrix, a priori information and an iteration time. The Ing process iteratively combines priori information from neighbours via the transformation matrix, and iteratively assigns an Ing score to each node to evaluate its influence. The algorithm appropriates for any types of networks, and includes some traditional centralities as special cases, such as degree, semi-local, LeaderRank. The Ing process converges in strongly connected networks with speed relying on the first two largest eigenvalues of the transformation matrix. Interestingly, the eigenvector centrality corresponds to a limit case of the algorithm. By comparing with eight renowned centralities, simulations of susceptible-infected-removed (SIR) model on real-world networks reveal that the Ing can offer more exact rankings, even without a priori information. We also observe that an optimal iteration time is always in existence to realize best characterizing of node influence. The proposed algorithms bridge the gaps among some existing measures, and may have potential applications in infectious disease control, designing of optimal information spreading strategies.

The Novelty of A-Web based Adaptive Data-Driven Networks (DDN) Management & Cooperative Communities on the Internet Technology

Nowadays, the area of adaptive data science of all data-driven properties on the Internet remains generally envision through integrated web entity maintenance. In this connection, several clients can collaborate with web server then collapse all data resources. However, the ideal client/server model tolerates after approximate edge produced via design all data in the unique centric area. Specifically, the proposed method of Internet cooperative communities is graphed data structure of vertical and horizontal entities sharing a mutual concern or field of reference. The computer networks centrally located the segment of cooperative neighbourhood build a logically graphs structure connection links spread the sensible computer networks structure of searching cooperative communities’ nodes on the Internet. The time for generation a global cooperative community structure can be improved and adjusted. That confesses the tool around dynamic and in state of the art algorithms’ and its usage performances. In this way, our techniques can professionally selection the classified structure of A-Web communities, and users preferred web data services can be recovered and choosing A-Web communities allowing to the categorised structure and distributes systems on influence rank. Finally, this is implemented into the novelty of A-Web constructed adaptive data-driven networks management structure. In the part of the contribution, this system provides the revolution of decentralised networking libraries. In other words, this project connects on the free-net and help in searching millions of scientific research data science volumes that are published globally on the Internet technology. This system also will connect other files; documents or info-resources on A-Web and middleware of the fundamental concepts of A-Web will be encapsulated transitory.

A New Method of Identifying Influential Users in the Micro-Blog Networks

Micro-blog services have become popular tools in the social networks. Online users discuss various topics in the micro-blog and some influential users can affect the opinions, attitudes, behaviors, or emotions of others. This paper proposes a user influence rank (UIRank) algorithm to identify the influential users through interaction information flow and interaction relationships among users in the micro-blog. The UIRank algorithm considers the contribution of user’s tweet and the characteristics of information dissemination in the micro-blog networks and calculates user influence score iteratively by user follower graph. Experimental results show that the UIRank algorithm outperforms other existing related algorithms in the precision, recall, and F1-Measure value.

Sensitivity Analysis and Calibration of a Rainfall-Runoff Model with the Combined Use of EPA-SWMM and Genetic Algorithm

An integrated Visual Basic Application interface is described that allows for sensitivity analysis, calibration and routing of hydraulichydrological models. The routine consists in the combination of three freeware tools performing hydrological modelling, hydraulic modelling and calibration. With such an approach, calibration is made possible even if information about sewers geometrical features is incomplete. Model parameters involve storage coefficient, time of concentration, runoff coefficient, initial abstraction and Manning coefficient; literature formulas are considered and manipulated to obtain novel expressions and variation ranges. A sensitivity analysis with a local method is performed to obtain information about collinearity among parameters and a ranking of influence. The least important parameters are given a fixed value, and for the remaining ones calibration is performed by means of a genetic algorithm implemented in GANetXL. Single-event calibration is performed with a selection of six rainfall events, which are chosen so to avoid non-uniform rainfall distribution; results are then successfully validated with a sequence of four events.

Influence analysis of Github repositories.

With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.

Identifying a set of influential spreaders in complex networks.

Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

The Impact of Power on Service Customers’ Willingness to Post Online Reviews

An increasing number of consumers rely on online reviews to make purchase decisions in today’s global service industry. In this article, we investigate the impact of power on a consumer’s willingness to spread word-of-mouth (WOM) by posting an online review. Drawing on self-enhancement theory, which suggests that an individual is motivated to spread WOM in order to boost his or her self-image, and the agentic versus communal theory of power, we argue that the impact of power is moderated by the valence of the customer’s service experience, and its congruity with that of other forum users. Results from three studies in hotel and restaurant contexts indicate that powerless consumers are more likely to post positive reviews when the forum consensus is also positive. Conversely, powerful consumers are more likely to post positive reviews when the overriding consensus is negative. Further, such results hold only for positive (vs. negative) service experiences. Managerially, our findings suggest that introducing power mechanisms such as influence rankings or helpful votes into online consumer forums may shape review posting behaviors. Service marketers and social media managers need to be aware that some customer segments are not inclined to post positive online reviews. As indicated by our findings, powerful consumers, such as those with high helpful ratings or elite reviewer status, are more likely to post positive reviews when the forum consensus is negative. Conversely, powerless consumers, such as those with low helpful ratings or junior reviewer status, might be more inclined to post positive reviews when the forum consensus is also positive. Based on our findings, we also discussed the theoretical contributions, limitations, and ideas for future research.

Pengaruh Masa Kerja, Usia, Pangkat dan Golongan Terhadap Motivasi Kerja Pegawai Negeri Sipil Pada Kantor SAR Pontianak

This study discusses the effect of length of service, age, rank and groups to work motivation of civil officers in Pontianak SAR office. The study was correlational, the data obtained through interviews, observations, and questionnaires, and secondary data. The population is all employees in the office and SAR Pontianak using saturated sample. Using the chi-square analysis. Research results revealed that there was an effect on the motivation of working time civil servants SAR Office of Pontianak, because Chi Square Count 4.90> 0.0075 Chi Square Table. There is an effect of age on the Civil Service Office motivation SAR Pontianak, because Chi Square Count 0.23> Chi Square Table 0.16. No influence Rank and type of motivation SAR Office of the Civil Service Pontianak, because Chi Square Count 13.02 <Chi Square Table 15.53.

Transcription factor motif quality assessment requires systematic comparative analysis.

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

Identification of Key Nodes in Microblog Networks

Microblog belongs to a new service of online social networks. For the perspective of information dissemination, the concept of spreading matrix is defined . A new WeiboR ank algorithm for identification of key nodes in the microblog networks is proposed taking into account parameters which includes the user direct appeal, the scope, and average information load. I n order to investigate how different measures correlate, we compare the relative influence rank of Top20 microblog users. The proposed algorithm is also compared with PageRank, Betweeness Centrality, Closeness Centrality, Out -degree algorithm using a new tweets propagation model ISR (Ignorants-Spreaders-Rejecters). Comparison results show that key nodes obtained from the WeiboRank algorithm have a wide r transmission range and better influence.

A review on user influence ranking factors in social networks

Social networks play a significant role in information sharing and have become an essential part of user's daily activities. Social networks help share users' ideas, views, status and opinions in the form of text, music and videos. The inherent difficulties involved in sharing the information is the sustenance of the values attached to the dependability of the functional blocks of the user - identity, sharing, conversation, relationship, presence, group and reputation. Review of social networks in terms of user influence ranking would throw light on the role of these functional blocks. The user influence ranking generally reveals the impact that an individual has on the social network. In the present scenario, the user influence ranking factors are not systematically determined. Instead, each social network adopts its own method to determine users' influence. Hence, the review has been done to identify the adoption of these functional blocks in popular social networks to determine users' influence ranking method.

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. Finally, we demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

THE IMPACT OF OCCUPANT BEHAVIOUR ON RESIDENTIAL GREENHOUSE GAS EMISSIONS REDUCTION

In 2011–12, greenhouse gas emissions from the Australian residential sector were 101.6 Mt and are expected to grow by 38% by 2050. In order to reduce these emissions, much emphasis has been placed on increasing the energy efficiency of buildings and appliances. Occupant behaviour, however, is probably the single most significant factor which determines energy use and emissions. This paper describes research undertaken to rank the most common occupant behaviours, based upon their impact on greenhouse gas emissions associated with residential energy use, in an architect-designed house in Australia. The occupant behaviours investigated were changing: the heating and cooling temperature set points, window openings, external blind use and lighting use. Simulations were carried out using Primero and EnergyPlus software. Based on the simulation results of greenhouse gas emissions, the following ranking of overall influence (from most influential to the least) has been determined: external blind use was one of the most effective measures to reduce emissions. Cooling set point temperature was similarly important with the magnitude of impact depending on the set point e.g. a 2°C increase had an impact comparable to the use of external blinds. The impact of the heating set point temperature was also dependent on the set point and overall slightly lower compared to the cooling set point temperature. Lighting use was the least influential parameter in the context of this study.