Sharif Digital Repository

Recommender systems have become an important issue in network science. Collaborative filtering and its variants are the most widely used approaches for building recommender systems, which have received great attention in both academia and industry. In this paper, we studied the relationship between recommender systems and connectivity of users-items bipartite network. This results in a novel recommendation algorithm. In our method recommended items are selected based on the eigenvector corresponding to the algebraic connectivity of the graph - the second smallest eigenvalue of the Laplacian matrix. Since recommending an item to a user equals to adding a new link to the users-items bipartite... 

In this paper we investigated the structural properties of the ultra high voltage power transmission network of Iran. We modeled the power grid as a network with 105 nodes and 142 connection links. We found that the Iranian power grid displays a relatively moderate clustering coefficient-much larger than that of corresponding random networks-and small characteristics path length comparable to that of corresponding random networks; i.e. the power grid is a small-world network with exponential degree distribution. Global efficiency was considered as an indicator of grid's performance and the influence of random and intentional nodal failures on the efficiency was investigated. We also studied... 

Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and hasmany applications, such as friendship recommendation and identifyingmalicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based... 

Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy-novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a... 

In this paper, we study information cascade in networks with positive and negative edges. The cascade depth is correlated with community structure of signed networks where communities are defined such that positive inter-community and negative intra-community links are minimized. The cascade is initialized from a number of nodes that are selected randomly. Finally, the number of nodes that have participated in the cascade is interpreted as cascade depth; the more the number of such nodes, the more the depth of the cascade. We investigate influence of community structure (i.e.; percentage of inter-community positive and intra-community negative links) on the cascade depth. We find significant... 

In this paper, we used echo state networks - a class of recurrent neural networks - for prediction of drought based on remote sensing data. To this end, the drought index was obtained for a number of stations in different clime zones of Iran. For each station, we also extracted the corresponding vegetation indices based on satellite imagery. Our model takes the satellite-based features as input and outputs the severity of drought. One of the major challenges of echo state networks is optimization of the reservoir parameters. Here we used a method based on Kronecker product in order to substantially reduce the parameter space to be optimized. We then used various optimization techniques... 

In this letter we studied the epidemic spreading on scale-free networks assuming a limited budget for immunization. We proposed a general model in which the immunity of an individual against the disease depends on its immunized friends in the network. Furthermore, we considered the possibility that each individual might be eager to pay a price to buy the vaccine and become immune against the disease. Under these assumptions we proposed an algorithm for improving the performance of all previous immunization algorithms. We also introduced a heuristic extension of the algorithm, which works well in scale-free networks  

Many real-world networks have a modular structure, and their component may undergo random errors and/or intentional attacks. More devastating situations may happen if the network components have a limited load capacity; the errors and attacks may lead to a cascading component removal process, and consequently, the network may lose its desired performance. In this brief, we investigate the tolerance of cascading errors and attacks in modular small-world networks. This brief studies the size of the largest connected component of the networks when cascading errors or attacks occur. The robustness of the network is tested as a function of both the intermodular connection and intramodular... 

This paper introduces the connection-graph-stability method and uses it to establish a new lower bound on the algebraic connectivity of graphs (the second smallest eigenvalue of the Laplacian matrix of the graph) that is sharper than the previously published bounds. The connection-graph-stability score for each edge is defined as the sum of the lengths of the shortest paths making use of that edge. We prove that the algebraic connectivity of the graph is bounded below by the size of the graph divided by the maximum connection-graph-stability score assigned to the edges  

Complex networks serve as generic models for many biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. Real-world networks are composed of building blocks called motifs that are indeed specific subgraphs of (usually) small number of nodes. Network motifs are important in the functionality of complex networks, and the role of some motifs such as feed-forward loop in many biological networks has been heavily studied. On the other hand, many biological networks have shown some degrees of robustness in terms of their efficiency and connectedness against failures in their components. In this paper we... 

Extracting community structure of complex network systems has many applications from engineering to biology and social sciences. There exist many algorithms to discover community structure of networks. However, it has been significantly under-explored for networks with positive and negative links as compared to unsigned ones. Trying to fill this gap, we measured the quality of partitions by introducing a Map Equation for signed networks. It is based on the assumption that negative relations weaken positive flow from a node towards a community, and thus, external (internal) negative ties increase the probability of staying inside (escaping from) a community. We further extended the Constant... 

In this paper, the task is to determine whether an HTTP session buys an item, or not, and if so, which items will be purchased. An HTTP session is a series of item clicks. A session has type buy, if it buys at least one item, or non-buy otherwise. Accordingly, data is in (session, item, time) format, which tells us when an item is clicked or purchased during an HTTP session. The main challenge comes from the fact that (1) user information is not available for clicked or purchased items, which are merely tagged with anony-mous sessions, and (2) suggestions are highly temporal as they are suggested to sessions instead of users. In other words, users which are stable and identified are replaced... 

Control of collective behavior is one of the most desirable goals in many applications related to social networks analysis and mining. In this work we propose a simple yet effective algorithm to control opinion formation in complex networks. We aim at finding the best spreaders whose connection to a reasonable number of informed agents results in the best performance. We consider an extended version of the bounded confidence model in which the uncertainty of each agent is adaptively controlled by the network. A number of informed agents with the desired opinion value is added to the network and create links with other agents such that large portion of the network follows their opinions. We... 

In this paper, we present an algorithm for enhancing synchronizability of dynamical networks with prescribed degree distribution. The algorithm takes an unweighted and undirected network as input and outputs a network with the same node-degree distribution and enhanced synchronization properties. The rewirings are based on the properties of the Laplacian of the connection graph, i.e., the eigenvectors corresponding to the second smallest and the largest eigenvalues of the Laplacian. A term proportional to the eigenvectors is adopted to choose potential edges for rewiring, provided that the node-degree distribution is preserved. The algorithm can be implemented on networks of any sizes as... 

A new criterion for prediction of train derailment is presented in this paper. A 2 DOF wheel-set model is used to identify the main dynamic parameters that affect wheel-set derailment. Using these parameters and conventional definition of derailment coefficient, a new criterion for prediction of wheelset derailment is introduced. The proposed criterion, in addition to providing the required precision in prediction of wheelset derailment, it requires measurements which are easy to perform. To evaluate the capability of the new criterion in prediction of derailment, a full wagon model with 48 DOF was used. The wagon model is a 3-D, non-linear model of a train passenger car. The model includes... 

A new criterion for prediction of train derailment is presented in this article. A three-degrees-of-freedom (3 DOF) wheelset model is used to identify the main dynamic parameters that affect wheelset derailment. Using these parameters and conventional definition of derailment coefficient, a new criterion for prediction of wheelset derailment is introduced. The proposed criterion, in addition to providing the required precision in prediction of wheel set derailment, requires measurements that are easy to perform. To evaluate the capability of the new criterion in prediction of derailment, a full wagon model with 48 DOF moving on a track with different random irregularities was used. The track... 

Recommender systems has become increasingly important in online community for providing personalized services and products to users. Traditionally, performance of recommender algorithms has been evaluated based on accuracy and the focus of the research was on providing accurate recommendation lists. However, recently diversity and novelty of recommendation lists have been introduced as key issues in designing recommender systems. In general, novelty/diversity and accuracy do not go hand in hand. Therefore, designing models answering novelty/diversityaccuracy dilemma is one of the challenging problems in the context of practical recommender systems. In this paper, we first introduce the... 

In this paper we introduce a novel population-based binary optimization technique, which works based on consensus of interacting multi-agent systems. The agents, each associated with an opinion vector, are connected through a network. They can influence each other, and thus their opinions can be updated. The agents work collectively with their neighbors to solve an optimization task. Here we consider a specific opinion update rule and various topologies for the connection network. Our experiments on a number of benchmark non-convex cost functions show that ring topology results in the best performance as compared to others. We also compare the performance of the proposed method with a number... 

Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model... 

A new criterion for prediction of train derailment is presented in this paper. A 2 DOF wheel-set model is used to identify the main dynamic parameters that affect wheel-set derailment. Using these parameters and conventional definition of derailment coefficient, a new criterion for prediction of wheelset derailment is introduced. The proposed criterion, in addition to providing the required precision in prediction of wheelset derailment, it requires measurements which are easy to perform. To evaluate the capability of the new criterion in prediction of derailment, a full wagon model with 48 DOF was used. The wagon model is a 3-D, non-linear model of a train passenger car. The model includes...