Methods Of The Novel Algorithms For Community Detection

Social networks are growing day by day. For modular representation of Graph $G(V,E)$ first phase of the design issue is to modularize the network having border nodes\cite{newman2006modularity}. Boarder nodes

all of the cases the algorithms were able to find the cheapest path between the

Social networks make a strong impact on the way we promote businesses, collect the news and also meet new people or reconnect with others. Facebook is one of the prime social networks where society receives news. Spreading information is easier just

As humans constantly use digital media and computational devices in their daily lives, more and more digital traces are left behind. With the addition of mobile devices, the amount of data left behind has been exponentially growing. This data is essential to identify the networks that are made between people. SocioPatterns is a research collaboration that adopts a data-driven methodology with the aim of uncovering fundamental patterns in social dynamics and coordinated human activity. This is very hard for them because with the ever changing population and characteristics, technology must be evolved. To achieve its scientific goals,

Social networking is sharing information between members and this information can reach a wide audience. This information’s are focused information centered on conversation between groups with shared interest.

The evaluation of links amongst the users of a social network is one of the most common and convenient method to detect mechanisms. \textbf{Social-Graph} based techniques majorly make use of these links to detect Sybils in the network. A social-graph like its name suggests is a graph of all the users and the relationships among themselves in the given social network. The social graph used in Sybil-detection mechanism tries to analyze the network in a different aspect than the regular graph. The social-graph based techniques consider all the users of the network as the \textbf{nodes} in the graph and the users which can be trusted are called the \textbf{honest nodes}. \textbf{Sybil nodes} are the nodes that are or can be a potential threat

The objective of this article is twofold. First, we seek to determine how global parameters of the social network, such as average path length and clustering, affect diffusion processes. Second, we attempt to identify early

We call the contracts that belong to a cluster a member of that particular cluster. The k-prototype algorithm can update the cluster memberships γ_i^((0))given the centroids, and it can update the centroids given cluster memberships. This process is repeated until some conditions (explained in step1.4) happened. This process includes the following steps,

The outputs of the different algorithms in the context of our study are the following:

population based or metaheuristic algorithm. It used the successful characteristicsof bees in different section such as are employed, onlooker and scout bees. The number of employed bees or the onlooker bees is equal to the number of solutions inthe swarm. The employed, onlooker bees used for exploitation process for a given problem towards best solution space given in equation (3). While scout bees use forexploration process through the following strategy as given in equation (4). 〖 V〗_ij=x_ij+θ_ij (x_ij-x_kj ) (3)Where vij is a new solution in the neighbourhood of xij for the employed bees, k isa solution in the neighbourhood of i, Φ is a random number in the range [-1, 1]. 〖 x 〗_ij^rand=〖x

Here we are discussing some papers related to k-cover group queries clustering in geo-social networks.

In this section we present our computational results of the properties of the proposed socio-spatial network models and compare them with those of ER, SW, and BA networks when it is appropriate. More specifically, we measure clustering coefficient, network density, average path length, assortativity, transitivity, and min, max, mean, and standard deviation degree. While several algorithms have been proposed for some of these measures, for simplicity of reproducing the results, we use the algorithms that have been implemented in the Python’s Networkx library. All results are averaged over 25 independent model runs with different random seeds. It should be noted that in all analysis in this study we only consider undirected networks. Moreover, since the focus of this study is on the human social network, we limit our analysis to Onnela et al’s (2011) findings that α ≈ 1.5 and only consider three values of α = 1.2, 1.5, and 2 for our sensitivity analysis purposes.

In our future work, we will be calculating the values of support and confidence on the multimode cluster using Apriori pruning. In this algorithm every item is considered as a 1-itemset. The support for each item is calculated and compared against the input support value entered by the user. If the support values of the item sets are less than the input support then those candidates are discarded and the action rules discovery algorithm only runs on the remaining attributes. This process is continued for 2–item sets and 3 and so until we have the support values for the item sets less than the input value. For example if we get the input support value to be 5 and the 4 item set attributes lead to support value less than 5, then we will be considering only the attributes that form the 4 item sets. In this method, we are pruning the attributes before the action rules are discovered and the algorithm only run on the attributes that form the frequent item sets. This avoids unnecessary combination of rules within the attributes. Instead of generating the rules at a later stage after finding all the combinations of rules, this prunes early to avoid unnecessary combinations. This greatly increases the time complexity of the algorithm and as well as space complexity. We are also planning to run this algorithm in the MapReduce Framework similar to the one we have done in this paper. This further decreases the time complexity and the action rules discovery

On the opposite side, the author in this paper \cite{rezaei2014adaptive} proposed a two modified scheme where he had provided lower computational complexity algorithm with retaining MTM-SVD functionality.

In this section, we propose an edge weight calculation method based on the location and social connection between the two users. Our formula is based on Josang’s definition of trust. We have scaled our weights to [0, 1] where 0 represents no trust and 1 represents full trust.