In the past few years ridesharing has largely reshaped the transportation marketplace. It is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. In the current ridesharing research, social ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing are two active directions with important business implications. Simultaneously optimizing social cohesion and revenue is vital to a commercial ridesharing platform's sustainable development, which, however, has not been previously studied. In this paper, we first present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top- $k$ k Matching (PTkM) queries which retrieve the top- $k$ k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment.

Top- $k$ k Vehicle Matching in Social Ridesharing: A Price-Aware Approach

Vital nodes identification in social networks keeps attracting lots of attention in recent years because of its great theoretical and practical significance for many applications. Degree centrality is one of the most efficient neighborhood-based metrics and has been widely used because of its simplicity and low computation complexity. Many other centralities derived from degree have been proposed to try to further improve the accuracy on ranking performance by extending the coverage area of neighbors. But a common problem of degree and its extensional centralities is that the directivity of edges between nodes is not considered. In directed network, nodes’ importance are not only related with their neighbor numbers but also related with the relationships between the nodes and their neighbors. In this paper, we propose a modification method with an adjustable parameter α on degree to improve its ranking accuracy in directed networks. This method redefines the degree of a node by separately taking into account its out-degree and in-degree and uses parameter α to flexibly set the relative weights between out-degree and in-degree when evaluating nodes’ importance in different scenes, which can effectively avoid the situation that too many neighbors from one direction could lead to extreme high value of centrality. We also apply this method on another two centralities: semi-local and k-hop, and for the two centralities that considering multi-step neighbors, the corresponding improvement centralities could eliminate the irrelevant multi-step neighbors when evaluating nodes’ importance. Experimental results in 3 real social networks and 1 artificial network indicate that, setting α to be appropriate value the improvement centralities can better estimate nodes’ connection abilities, propagation abilities and immunization abilities while keeping the same compute complexity with the corresponding original centralities, especially in the network with low percentage of bidirectional edges. Moreover, the improvement method proposed in this paper also can be applied to other centralities, such as eigenvector centrality, neighborhood centrality, Exdegree centrality and percolation based centrality, to improve their performances in directed networks.

An improvement method for degree and its extending centralities in directed networks

Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their satisfactory performance is not without prior anchor link information and/or node attributes, which may not always be available. In this paper, we propose Grad-Align+, a novel NA method using node attribute augmentation that is quite robust to the absence of such additional information. Grad-Align+ is built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers only a part of node pairs until all node pairs are found. Specifically, Grad-Align+ is composed of the following key components: 1) augmenting node attributes based on nodes' centrality measures, 2) calculating an embedding similarity matrix extracted from a graph neural network into which the augmented node attributes are fed, and 3) gradually discovering node pairs by calculating similarities between cross-network nodes with respect to the aligned cross-network neighbor-pair. Experimental results demonstrate that Grad-Align+ exhibits (a) superiority over benchmark NA methods, (b) empirical validation of our theoretical findings, and (c) the effectiveness of our attribute augmentation module.

/pdf/gradalign-empowering-gradual-network-alignment-using-1jdc59to.pdf

GradAlign+: Empowering Gradual Network Alignment Using Attribute Augmentation

Betweenness centrality is a metric to measure the relative importance of vertices within a graph. The computation of betweenness centrality is based on shortest paths which requires O(n+m) space and O(mn) and O(nm+n2 log n) time on unweighted and weighted graphs, respectively. It is time-consuming to deal with large-scale graphs, which motivates us resort to distributed computing and parallel algorithms. In this paper, we design a vertex-based parallel algorithm following the shortest path approach (SPBC). Moreover, we propose a distributed algorithm based on message propagation(MPBC) to quantify the importance of vertices. MPBC takes into account the real situation of information diffusion in social networks. We implement our algorithms on Graphlab and evaluate them through comprehensive experiments. The results show that both SPBC and MPBC scale well with the increasing number of machines. SPBC on 2 machines outperforms the classical centralized algorithm by 1.59 times in terms of running time. MPBC can handle graph with ten millions of vertices and edges within an acceptable time where classical algorithms become infeasible.

Vertex-centric Parallel Algorithms for Identifying Key Vertices in Large-Scale Graphs

MOVES: A MemOry-based VEhicular Social forwarding technique

Identifying the most influential spreaders in social networks has many practical applications. The existing methods for the purpose are either too time-consuming for dynamic large-scale networks, such as betweenness centrality, closeness centrality, eigenvector centrality and Katz centrality, or do not consider the network topology, such as degree centrality. To design an effective method to identify the most influential nodes in a network, we propose a novel metric, k-hop centrality which is a generalization of degree centrality. The k-hop index is the summation of the number n(i) of nodes within k-hop distance from the node in question, attenuated by 1/αi, for 1 ≤ i ≤ k (α is the average degree of nodes in the network). It is calculated in a localized manner and is complexity-scalable by adjusting the value of k, thus suitable for dynamically changing, large social networks. We adopt the Susceptible Infected Recovered (SIR) model to evaluate the performance of k-hop centrality over four real datasets of complex networks, and experimental results show that our method outperforms state-of-the-art methods in this field in terms of both infection ratios (spreading influence) and computational complexity. Our work sheds some light on designing efficient spreading strategies for complex networks.

Jinyang Fan

Papers

K-hop centrality metric for identifying influential spreaders in dynamic large-scale social networks