Small world networks and clustered small world networks with random connectivity

International audienceThe discovery of small world properties in real-world networks has revolutionized the way we analyze and study real-world systems. Mathematicians and physicists in particular have closely studied and developed several models to artificially generate networks with small world properties. The classical algorithms to produce these graphs artificially make use of the fact that with the introduction of some randomness in ordered graphs, small world graphs can be produced. In this paper, we present a novel algorithm to generate graphs with small world properties based on the idea that with the introduction of some order in a random graph, small world graphs can be generated. Our model starts with a randomly generated graph. We then replace each node of the random graph with cliques of different sizes. This ensures that the connectivity between the cliques is random but the clustering coefficient increases to a desired level. We further extend this model to incorporate the property of community structures (clusters) found readily in real-world networks such as social, biological and technological networks. These community structures are densely connected regions of nodes in a network that are loosely connected to each other. The model generates these clustered small world graphs by replacing nodes in the random graph with densely connected set of nodes. Experimentation shows that these two models generate small world and clustered small world graphs, respectively, as we were able to produce the desired properties of a small world network with high clustering coefficient and low average path lengths in both cases. Furthermore, we also calculated relative density and modularity to show that the clustered networks indeed had community structures

Tunable and Growing Network Generation Model with Community Structures

Recent years have seen a growing interest in the modeling and simulation of social networks to understand several social phenomena. Two important classes of networks, small world and scale free networks have gained a lot of research interest. Another important characteristic of social networks is the presence of community structures. Many social processes such as information diffusion and disease epidemics depend on the presence of community structures making it an important property for network generation models to be incorporated. In this paper, we present a tunable and growing network generation model with small world and scale free properties as well as the presence of community structures. The major contribution of this model is that the communities thus created satisfy three important structural properties: connectivity within each community follows power-law, communities have high clustering coefficient and hierarchical community structures are present in the networks generated using the proposed model. Furthermore, the model is highly robust and capable of producing networks with a number of different topological characteristics varying clustering coefficient and inter-cluster edges. Our simulation results show that the model produces small world and scale free networks along with the presence of communities depicting real world societies and social networks.Comment: Social Computing and Its Applications, SCA 13, Karlsruhe : Germany (2013

A Camel Driver In The Achaemenid Persian Empire: Social Network Analysis Of A Non‐Elite Professional And The Persepolis Fortification Archive

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135485/1/bics12035.pd

Συγκριτική ανάλυση μεθόδων μείωσης διάστασης και εκμάθησης πολλαπλοτήτων για την ταξινόμηση δεδομένων Λειτουργικής Απεικόνισης Μαγνητικού Συντονισμού (FMRI)

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Εφαρμοσμένη Μηχανική

Electronic word of mouth in online social networks: strategies for coping with opportunities and challenges

In today's world, the widespread success of the Internet, social media, and online social networks (OSN) provide the basis for electronic word of mouth (EWOM). EWOM can be seen as a digital enhancement of traditional word of mouth that makes communication more efficient and involves less effort by its users. The resulting speed of diffusion and information transparency have caused transformative changes in consumer behaviour in all types of markets, which requires the development of new business strategies for adequately dealing with the new circumstances. This doctoral dissertation is divided into three overall subject areas that concern the investigation of capable strategies for coping with the emerged opportunities and challenges of EWOM in OSN. The first subject area concerns negative electronic word of mouth in OSN and investigates capable countermeasure strategies for firms to adequately address claims of unsatisfied customers. For this, three simulation studies are conducted in which the propagation of a negative message and its countering by a positive message published by the firm are numerically analysed. The results reveal that, in general, the persuasiveness of a firm's response is more important than a quick response with a less persuasive counter-message. To some extent, this also holds if the number of OSN members who initially disseminate the counter-message on behalf of the firm is increased. In the second subject area, an optimisation model for individualised pricing is developed for an online store whose customers are interconnected in an OSN and can share price information via EWOM. The model is solved numerically by artificial intelligence solution methods. The results indicate that personalised prices can be financially worthwhile even under price transparency. The third subject area investigates market entry strategies for social media apps and services that are advertised in an OSN for acquiring new users and examines the role of EWOM in this context. A diffusion model is developed and analysed numerically by simulation. Three different targeting approaches are compared to each other regarding their ability to reach a high share of active users in the OSN: (1) a random marketing strategy, where randomly chosen members in the OSN are presented the advertisement, (2) cluster marketing, where whole clusters of members who are densely connected to each other are simultaneously shown the advertisement, and (3) influencer marketing, where the most influential users in the OSN are selected to share sponsored posts about the app in the OSN. The results suggest that EWOM can have detrimental effects if OSN members are too early informed about the app or service. If the information about the app reaches clusters in the OSN prematurely where a sufficient level of activity is not present yet, it can deplete the excitement of the users. The lack of excitement, in turn, can significantly reduce the effect of subsequent marketing campaigns. However, if applied appropriately, a higher level of EWOM about the app or service can increase the performance of the random marketing strategy to the extent that it outperforms cluster and influencer marketing