scholarly journals Point-process models of social network interactions: Parameter estimation and missing data recovery

2015 ◽  
Vol 27 (3) ◽  
pp. 502-529 ◽  
Author(s):  
JOSEPH R. ZIPKIN ◽  
FREDERIC P. SCHOENBERG ◽  
KATHRYN CORONGES ◽  
ANDREA L. BERTOZZI
Keyword(s):  
Social Networks ◽  
Parameter Estimation ◽  
Social Network ◽  
Missing Data ◽  
Process Model ◽  
The United States ◽  
Process Models ◽  
Likelihood Method ◽  
Electronic Communications ◽  
Data Set

Electronic communications, as well as other categories of interactions within social networks, exhibit bursts of activity localised in time. We adopt a self-exciting Hawkes process model for this behaviour. First we investigate parameter estimation of such processes and find that, in the parameter regime we encounter, the choice of triggering function is not as important as getting the correct parameters once a choice is made. Then we present a relaxed maximum likelihood method for filling in missing data in records of communications in social networks. Our optimisation algorithm adapts a recent curvilinear search method to handle inequality constraints and a non-vanishing derivative. Finally we demonstrate the method using a data set composed of email records from a social network based at the United States Military Academy. The method performs differently on this data and data from simulations, but the performance degrades only slightly as more information is removed. The ability to fill in large blocks of missing social network data has implications for security, surveillance, and privacy.

Patterns of frequent user interactions in blogosphere

2020 ◽  
Author(s):  
Krzysztof Rudek ◽  
Jarosław Koźlak
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Relative Difference ◽  
Pattern Identification ◽  
Frequent Patterns ◽  
Frequent User ◽  
User Interactions ◽  
Data Set ◽  
Political Issues

Abstract The aim of the paper is to identify and categorize frequent patterns describing interactions between users in social networks. We analyze a social network with relationships between users that evolve in time already identified. In our research, we discover patterns based on frequent interactions between groups of users. The patterns are described by the characteristics of these interactions, such as their reciprocity, or the relative difference between estimations of global influences of the users participating in the discussions. The modification of the apriori algorithms is applied as one of the methods for pattern identification. The analyzed social network is built using the data set containing data from the Polish blog website salon24, which concerns mostly socio-political issues.

Centrality measure based approach for detection of malicious nodes in twitter social network

2018 ◽  
Vol 7 (4.5) ◽  
pp. 518 ◽  
Author(s):  
Krishna Das ◽  
Smriti Kumar Sinha
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Opinion Mining ◽  
Theoretical Method ◽  
Short Paper ◽  
Malicious Nodes ◽  
Data Set ◽  
Centrality Measure ◽  
The Social

In this short paper, network structural measure called centrality measure based mathematical approach is used for detection of malicious nodes in twitter social network. One of the objectives in analysing social networks is to detect malicious nodes which show anomaly behaviours in social networks. There are different approaches for anomaly detection in social networks such as opinion mining methods, behavioural methods, network structural approach etc. Centrality measure, a graph theoretical method related to social network structure, can be used to categorize a node either as popular and influential or as non-influential and anomalous node. Using this approach, we have analyzed twitter social network to remove anomalous nodes from the nodes-edges twitter data set. Thus removal of these kinds of nodes which are not important for information diffusion in the social network, makes the social network clean & speedy in fast information propagation.   

Defining the Factors that Effect User Interest on Social Network News Feeds via Fuzzy Association Rule Mining

2013 ◽  
pp. 334-345
Author(s):  
Basar Öztaysi ◽  
Sezi Çevik Onar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Data Set ◽  
Frequent Items ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining

Social networking became one of the main marketing tools in the recent years since it’s a faster and cheaper way to reach the customers. Companies can use social networks for efficient communication with their current and potential customers but the value created through the usage of social networks depends on how well the organizations use these tools. Therefore a support system which will enhance the usage of these tools is necessary. Fuzzy Association rule mining (FARM) is a commonly used data mining technique which focuses on discovering the frequent items and association rules in a data set and can be a powerful tool for enhancing the usage of social networks. Therefore the aim of the chapter is to propose a fuzzy association rule mining based methodology which will present the potential of using the FARM techniques in the field of social network analysis. In order to reveal the applicability, an experimental evaluation of the proposed methodology in a sports portal will be presented.

scholarly journals PRICING PRECIPITATION BASED DERIVATIVES

2005 ◽  
Vol 08 (07) ◽  
pp. 959-988 ◽  
Author(s):  
RENÉ CARMONA ◽  
PAVEL DIKO
Keyword(s):  
Parameter Estimation ◽  
Process Model ◽  
Stochastic Dynamics ◽  
Model Fitting ◽  
Process Models ◽  
Explicit Solutions ◽  
Power Utility ◽  
Specific Location ◽  
Markov Process Model ◽  
Utility Indifference

We consider the problem of pricing a derivative contract written on precipitation at a specific location during a given period of time. We propose a jump Markov process model for the stochastic dynamics of the underlying precipitation. Our model is based on pulse Poisson process models widely used in hydrology. We develop maximum likelihood parameter estimation procedures to fit our model to rainfall data. In order to price derivatives, we assume the existence of a traded asset whose price dynamics are influenced by the precipitation at the location in question, and we rely on the utility indifference approach. Two utility functions are considered: exponential and power utility. We derive explicit solutions for the exponential and bounds for the power utility. Finally, we apply our model fitting and pricing techniques to a sample rainfall contract in Norway.

The Personal Social Networks of Resettled Bhutanese Refugees During Pregnancy in the United States: A Social Network Analysis

2018 ◽  
Vol 43 (6) ◽  
pp. 1028-1036 ◽  
Author(s):  
Diana M. Kingsbury ◽  
Madhav P. Bhatta ◽  
Brian Castellani ◽  
Aruna Khanal ◽  
Eric Jefferis ◽  
...  
Keyword(s):  
United States ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The United States ◽  
Bhutanese Refugees ◽  
Personal Social Networks

Semantic network edges: a human-machine approach to represent typed relations in social networks

2015 ◽  
Vol 19 (1) ◽  
pp. 71-81 ◽  
Author(s):  
M. Cristina Pattuelli ◽  
Matthew Miller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Network ◽  
Contextual Information ◽  
Open Data ◽  
Entity Recognition ◽  
Semantic Web Technologies ◽  
Data Set ◽  
Content Type ◽  
External Data

Purpose – The purpose of this paper is to describe a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. Design/methodology/approach – A multi-method approach was applied including automated named entity recognition and extraction to create a social network, and crowdsourcing techniques to semantically enhance the data through the classification of relations and the integration of contextual information. Linked open data standards provided the knowledge representation technique for the data set underlying the network. Findings – The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real-world scenario. The data set from which the network is based is accessible via an application programming interface and, thus, shareable with the knowledge management community for reuse and mash-ups. Originality/value – This paper presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying data set, making the data readable across platforms and linkable with external data sets. This approach has the potential to make social networks dynamic and open to integration with external data sources.

The impact of organizational social networks on salespeople’s negative headquarters stereotypes

2020 ◽  
Vol 35 (12) ◽  
pp. 1901-1913
Author(s):  
Babak Hayati ◽  
Sandeep Puri
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sales Performance ◽  
Sales Managers ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
Network Patterns ◽  
The Impact ◽  
Sales Forces

Purpose Extant sales management literature shows that holding negative headquarters stereotypes (NHS) by salespeople is harmful to their sales performance. However, there is a lack of research on how managers can leverage organizational structures to minimize NHS in sales forces. This study aims to know how social network patterns influence the flow of NHS among salespeople and sales managers in a large B2B sales organization. Design/methodology/approach The authors hypothesize and test whether patterns of social networks among salespeople and sales managers determine the stereotypical attitudes of salespeople toward corporate directors and, eventually, impact their sales performance. The authors analyzed a multi-level data set from the B2B sales forces of a large US-based media company. Findings The authors found that organizational social network properties including the sales manager’s team centrality, sales team’s network density and sales team’s external connectivity moderate the flow of NHS from sales managers and peer salespeople to a focal salesperson. Research limitations/implications First, the data was cross-sectional and did not allow the authors to examine the dynamics of social network patterns and their impact on NHS. Second, The authors only focused on advice-seeking social networks and did not examine other types of social networks such as friendship and trust networks. Third, the context was limited to one company in the media industry. Practical implications The authors provide recommendations to sales managers on how to leverage and influence social networks to minimize the development and flow of NHS in sales forces. Originality/value The findings advance existing knowledge on how NHS gets shared and transferred in sales organizations. Moreover, this study provides crucial managerial insights with regard to controlling and managing NHS in sales forces.

scholarly journals PREDICTING TRUST IN A SOCIAL NETWORK BASED ON STRUCTURAL SIMILARITIES USING A MULTI-LAYERED PERCEPTRON NEURAL NETWORK

2021 ◽  
Vol 22 (1) ◽  
pp. 103-117
Author(s):  
Amir Hossein Danesh ◽  
Hossein Shirgahi
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Network ◽  
Previous History ◽  
Structural Similarity ◽  
Training Data ◽  
Negative Effects ◽  
Data Set ◽  
Trained Neural Network ◽  
The Web

Although research on social networks is progressing rapidly, the positive and negative effects of this area should be evaluated. One of the problems is that social networks are very broad and anyone can have influence on them. This matter can cause the issue of people with different beliefs. Therefore, determining the amount of trust to various resources on social networks, and especially resources for which there is no previous history on the web, is one of the main challenges in this field. In this paper, we present a method for predicting trust in a social network by structural similarities through the neural network. In this method, the web of trust data set is converted to a structural similarity data set based on the similarity of the trustors and trustees first. Then, on the created data set, a part of the data set is considered as the training data and it is trained based on the multilayer perceptron neural network and then the trained neural network is tested based on the test data. In the proposed method, the MSE value is less than 0.01, which has improved more than 0.02 compared to previous methods. Based on the obtained results, the proposed method has provided acceptable accuracy. ABSTRAK: Walaupun kajian tentang rangkaian sosial adalah sangat pesat, kesan positif dan negatif dalam ruang lingkup ini perlu dinilai. Masalah rangkaian sosial adalah sangat luas dan sesiapa sahaja boleh terpengaruh. Perkara ini akan menyebabkan manusia dengan pelbagai isu kepercayaan. Oleh itu, menentukan nilai kepercayaan melalui pelbagai sumber dalam rangkaian sosial, terutama sumber-sumber yang tidak mempunyai sejarah lepas dalam web, adalah salah satu cabaran dalam bidang ini. Kajian ini membentangkan jangkaan kepercayaan dalam rangkaian sosial melalui persamaan struktur dengan menggunakan rangkaian neural. Kaedah ini ditentukan dengan menukar set data web kepercayaan kepada struktur set data hampir sama berdasarkan kesamaan pemegang dan pemberi amanah. Kemudian, sebilangan set data yang telah dibina ini dipertimbangkan sebagai data latihan dan ia dilatih berdasarkan rangkaian neural perseptron berbagai lapisan dan kemudian rangkaian neural yang terlatih ini diuji berdasarkan data ujian. Dalam kaedah yang dicadangkan ini, nilai MSE adalah kurang daripada 0.01, di mana telah diperbaiki kepada 0.02 lebih daripada kaedah-kaedah sebelum ini. Berdasarkan dapatan kajian, didapati kaedah yang dicadangkan ini menunjukkan ketepatan yang boleh diterima.

Student-Faculty Communication on Facebook

2013 ◽  
pp. 40-67
Author(s):  
Laurentiu Soitu ◽  
Laura Paulet-Crainiceanu
Keyword(s):  
United States ◽  
Social Networks ◽  
Social Network ◽  
Empirical Study ◽  
Theoretical Perspective ◽  
The United States ◽  
Positive Attitudes ◽  
Facebook Use ◽  
Learning Issues ◽  
Networks Integration

This chapter addresses the topic of Facebook use in education, with focus on the learning issues concerning the student-faculty relations and communication on this social network. Its main purpose is to reveal academics’ general and particular attitudes towards the use of Facebook with instructional aim. Therefore, it presents a generous theoretical perspective on the emerging phenomenon of Social Networks integration on education, in the United States mainly. Further on, it puts side by side these views with the findings of a particular, empirical study conducted by the authors. A survey applied to a sample of Facebook users from “Alexadru Ioan Cuza” University of Iasi, Romania (N=160) revealed that the academics partially agreed that the use of Facebook is suitable for educational exchanges. Whilst the literature suggested that students have more positive attitudes than faculty towards the use of Facebook in education, this present study does not support this view.

Mining business rules from business process model repositories

2015 ◽  
Vol 21 (4) ◽  
pp. 820-836 ◽  
Author(s):  
Jantima Polpinij ◽  
Aditya Ghose ◽  
Hoa Khanh Dam
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Sequential Patterns ◽  
Business Rules ◽  
Data Set ◽  
Content Type ◽  
Market Growth ◽  
Business Process Models

Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.

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