Virtual Social Networks Online and Mobile Systems

Location-based applications are one of the most anticipated new segments of the mobile industry. These new applications are enabled by GPS-equipped phones (e.g., emergency applications, buddy finders, games, location-based advertising, etc.). These services are designed to give consumers instant access to personalized, local content of their immediate location. Some applications couple LBS with notification services, automatically alerting users when they are close to a pre-selected destination. With the advances in the Internet and communications/mobile technology, it became vital to analyze the effect of such technologies on human communications. This work studies how humans can construct social networks as a method for group communications using the available technologies. We constructed and analyzed a friends network using different parameters. The parameters that were calculated to analyze the network are the distribution sequence, characteristic path length, clustering coefficient and centrality measures. In addition, we built a PDA application that implements the concept of LBS using two system modules. In the first module, we have developed an application for entertainment purpose; an application program which enables end users to send their birth year and get their horoscope in return. The second part of the project was, to build an application, which helps people to stay in touch with their friends and family members (Find Friend). It helps users to find which of their buddies are within the same area they are in.

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Privacy Preserving in Social Networks Using Combining Cuckoo Optimization Algorithm and Graph Clustering for Anonymization

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2019/v3i330092 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Mehdi Namdarzadegan ◽

Taleb Khafaei

Keyword(s):

Social Networks ◽

Social Networking ◽

Optimization Algorithm ◽

Social Networking Sites ◽

Path Length ◽

Virtual Communities ◽

Clustering Coefficient ◽

The Internet ◽

Clustering Method ◽

Cuckoo Optimization Algorithm

Recently, social networks have received dramatic interest. The speed of the development and expansion of the Internet has created a new topic of research called social networks or online virtual communities on the Internet. Today, social networking sites such as Facebook, Twitter, Instagram and so forth are dramatically used by many people. Since people publish a lot of information about themselves on these networks, this information may be attacked by the intruders, so the need of preserving privacy is necessary on these networks. One of the approaches for preserving privacy is the K-anonymity. Anonymization always faces the challenge of data lost, therefore, an approach is required for anonymization of data and meanwhile maintaining the usefulness of the data. In this research, by combining the k-anonymity priority clustering method and Cuckoo optimization algorithm, an appropriate model is developed to maintain the privacy of the data and its usefulness. The average path length, average clustering coefficient and the transitivity criteria have been used to evaluate the proposed algorithm. The results of the experiments show that the proposed method in most cases has 1 unit superiority in terms of k-anonymity and 2 units superiority in terms of usefulness in comparison with similar methods.

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Topological measures for identifying and predicting the spread of complex contagions

Nature Communications ◽

10.1038/s41467-021-24704-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Douglas Guilbeault ◽

Damon Centola

Keyword(s):

Social Networks ◽

Shortest Path ◽

Empirical Data ◽

Path Length ◽

Centrality Measures ◽

Social Phenomena ◽

Node Centrality ◽

Network Connectedness ◽

Topological Measures ◽

Definition Of

AbstractThe standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

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A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Complexity ◽

10.1155/2018/1528341 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Chengying Mao ◽

Weisong Xiao

Keyword(s):

Social Networks ◽

Random Walk ◽

Preference Relation ◽

Disease Outbreak ◽

Centrality Measures ◽

The Internet ◽

Virus Propagation ◽

Preference Graph ◽

Partial Preference ◽

Relation Analysis

In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.

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Domestic vs. International E-Shopping

Advances in Human and Social Aspects of Technology - Handbook of Research on Technology Integration in the Global World ◽

10.4018/978-1-5225-6367-9.ch002 ◽

2019 ◽

pp. 24-39

Author(s):

Vaggelis Saprikis

Keyword(s):

Social Networks ◽

Information And Communication Technologies ◽

Communication Technologies ◽

Business Environment ◽

The Internet ◽

Internet Users ◽

Mobile Industry ◽

Information And Communication ◽

Number Of Individuals ◽

Shed Light

Contemporary commerce is completely different as regards features some years ago. Nowadays, a considerable number of individuals and firms take advantage of the information and communication technologies and conduct transactions online. In particular, the mobile industry along with the broad use of social networks and improvements in the internet bandwidth worldwide has created a completely different business environment. Consequently, the technology incited many consumers to cross-border e-shopping, allowing access to a wider variety of products and services, and in numerous circumstances, access to cheaper goods. The purpose of this chapter is to investigate the perceptions internet users have towards e-shops focusing on Greece. More precisely, it aims to find out whether there are contingent differences on customers' perceptions regarding domestic vs. international e-shops, since a gradually augmented number of people have been expressing their preference on non-domestic e-stores for their purchases. Additionally, the chapter intends to shed light on the difficulty in understanding vital aspects of e-consumer behaviour.

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Domestic vs. International E-Shopping

Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business ◽

10.4018/978-1-7998-8957-1.ch090 ◽

2021 ◽

pp. 1819-1834

Author(s):

Vaggelis Saprikis

Keyword(s):

Social Networks ◽

Information And Communication Technologies ◽

Communication Technologies ◽

Business Environment ◽

The Internet ◽

Internet Users ◽

Mobile Industry ◽

Information And Communication ◽

Number Of Individuals ◽

Shed Light

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A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.774607 ◽

2021 ◽

Vol 13 ◽

Author(s):

Cuibai Wei ◽

Shuting Gong ◽

Qi Zou ◽

Wei Zhang ◽

Xuechun Kang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Path Length ◽

Brain Networks ◽

Brain Regions ◽

Clustering Coefficient ◽

Characteristic Path Length ◽

Glucose Intake ◽

Item Scores ◽

Structural Brain

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

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Disruption of Cortical Connectivity during Remifentanil Administration Is Associated with Cognitive Impairment but Not with Analgesia

Anesthesiology ◽

10.1097/aln.0000000000000510 ◽

2015 ◽

Vol 122 (1) ◽

pp. 140-149 ◽

Cited By ~ 14

Author(s):

Ahmad Khodayari-Rostamabad ◽

Søren S. Olesen ◽

Carina Graversen ◽

Lasse P. Malver ◽

Geana P. Kurita ◽

...

Keyword(s):

Reaction Time ◽

Functional Connectivity ◽

Path Length ◽

Experimental Pain ◽

Clustering Coefficient ◽

Cognitive Test ◽

Characteristic Path Length ◽

Network Properties ◽

Continuous Reaction ◽

Continuous Reaction Time

Abstract Background: The authors investigated the effect of remifentanil administration on resting electroencephalography functional connectivity and its relationship to cognitive function and analgesia in healthy volunteers. Methods: Twenty-one healthy male adult subjects were enrolled in this placebo-controlled double-blind cross-over study. For each subject, 2.5 min of multichannel electroencephalography recording, a cognitive test of sustained attention (continuous reaction time), and experimental pain scores to bone-pressure and heat stimuli were collected before and after infusion of remifentanil or placebo. A coherence matrix was calculated from the electroencephalogram, and three graph-theoretical measures (characteristic path-length, mean clustering coefficient, and relative small-worldness) were extracted to characterize the overall cortical network properties. Results: Compared to placebo, most graph-theoretical measures were significantly altered by remifentanil at the alpha and low beta range (8 to 18 Hz; all P < 0.001). Taken together, these alterations were characterized by an increase in the characteristic path-length (alpha 17% and low beta range 24%) and corresponding decrements in mean clustering coefficient (low beta range −25%) and relative small-worldness (alpha −17% and low beta range −42%). Changes in characteristic path-lengths after remifentanil infusion were correlated to the continuous reaction time index (r = −0.57; P = 0.009), while no significant correlations between graph-theoretical measures and experimental pain tests were seen. Conclusions: Remifentanil disrupts the functional connectivity network properties of the electroencephalogram. The findings give new insight into how opioids interfere with the normal brain functions and have the potential to be biomarkers for the sedative effects of opioids in different clinical settings.

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Interactions Between Aging and Alzheimer’s Disease on Structural Brain Networks

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.639795 ◽

2021 ◽

Vol 13 ◽

Author(s):

Zhanxiong Wu ◽

Yunyuan Gao ◽

Thomas Potter ◽

Julia Benoit ◽

Jian Shen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Path Length ◽

Clustering Coefficient ◽

Network Efficiency ◽

Characteristic Path Length ◽

Structural Networks ◽

Structural Brain

Normative aging and Alzheimer’s disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56–65, 66–75, and 71–85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

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Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

10.1101/2021.03.09.434656 ◽

2021 ◽

Author(s):

Yiran Wei ◽

Chao Li ◽

Zaixu Cui ◽

Roxanne C. Mayrand ◽

Jingjing Zou ◽

...

Keyword(s):

Path Length ◽

Clustering Coefficient ◽

Survival Models ◽

Characteristic Path Length ◽

Network Construction ◽

Topological Features ◽

Structural Connectome ◽

Newly Diagnosed Glioblastoma ◽

Brain Parenchymal ◽

Connectivity Strength

AbstractBackgroundGlioblastoma is characterized by extensive invasion into brain parenchymal tissue through white matter tracts. Systematically quantifying invasion, however, is limited by the conventional imaging tools, and could potentially be achieved by a structural connectome approach.MethodsTwo prospective patient cohorts of newly diagnosed glioblastoma were included for network construction. A fiber template was firstly derived by employing probabilistic tractography on healthy subjects. Through performing tract-based spatial statistics in patients and age-matched controls, the connectivity strength of each fiber was estimated in patients for network construction. Contrast-enhancing and non-enhancing tumors were segmented and overlaid to the network to identify connectome disruption in lesion and distant areas. The connectome disruption probabilities were calculated across all patients. Disruption indices and network topological features were examined using survival models.ResultsThe distant areas accounted for higher proportion of disruption than the contrast-enhancing tumor (16.8 ± 12.0% vs 5.8 ± 5.1%, P < 0.001). Compared to healthy controls, patient networks demonstrated lower clustering coefficient, but higher characteristic path length (each P < 0.001). Higher distant area disruption (HR = 1.43, P = 0.027) and characteristic path length (HR = 1.59, P = 0.031) were associated with worse survival, while higher clustering coefficient (HR = 0.59, P = 0.016) was associated with prolonged survival.ConclusionThe occult invasion in glioblastoma could be identified and quantified using structural connectome, which may confer benefits to precise patient management.

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Functional Connectivity of EEG in Encephalitis during Slow Biphasic Complexes

Electronics ◽

10.3390/electronics10232978 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2978

Author(s):

Giovanni Chiarion ◽

Luca Mesin

Keyword(s):

Functional Connectivity ◽

Path Length ◽

Inflammatory Diseases ◽

Clustering Coefficient ◽

Eeg Rhythms ◽

Characteristic Path Length ◽

Slowing Down ◽

Eeg Functional Connectivity ◽

Network Patterns ◽

The Brain

The electroencephalogram (EEG) of patients suffering from inflammatory diseases of the brain may show specific waveforms called slow biphasic complexes (SBC). Recent studies indicated a correlation between the severity of encephalitis and some features of SBCs, such as location, amplitude and frequency of appearance. Moreover, EEG rhythms were found to vary before the onset of an SBC, as if the brain was preparing to the discharge (actually with a slowing down of the EEG oscillation). Here, we investigate possible variations of EEG functional connectivity (FC) in EEGs from pediatric patients with different levels of severity of encephalitis. FC was measured by the maximal crosscorrelation of EEG rhythms in different bipolar channels. Then, the indexes of network patterns (namely strength, clustering coefficient, efficiency and characteristic path length) were estimated to characterize the global behavior when they are measured during SBCs or far from them. EEG traces showed statistical differences in the two conditions: clustering coefficient, efficiency and strength are higher close to an SBC, whereas the characteristic path length is lower. Moreover, for more severe conditions, an increase in clustering coefficient, efficiency and strength and a decrease in characteristic path length were observed in the delta–theta band. These outcomes support the hypothesis that SBCs result from the anomalous coordination of neurons in different brain areas affected by the inflammation process and indicate FC as an additional key for interpreting the EEG in encephalitis patients.

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