Youfang Lin

Balanced multi-label propagation for overlapping community detection in social networks

In this paper, we propose a balanced multi-label propagation algorithm (BMLPA) for overlapping community detection in social networks. As well as its fast speed, another important advantage of our method is good stability, which other multi-label propagation algorithms, such as COPRA, lack. In BMLPA, we propose a new update strategy, which requires that community identifiers of one vertex should have balanced belonging coefficients. The advantage of this strategy is that it allows vertices to belong to any number of communities without a global limit on the largest number of community memberships, which is needed for COPRA. Also, we propose a fast method to generate “rough cores”, which can be used to initialize labels for multi-label propagation algorithms, and are able to improve the quality and stability of results. Experimental results on synthetic and real social networks show that BMLPA is very efficient and effective for uncovering overlapping communities.

Link prediction with node clustering coefficient

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed Cannistrai–Alanis–Ravai (CAR) index shows the power of local link/triangle information in improving link-prediction accuracy. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common-neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is that it employs links existing between neighbors of a common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. In our experiments, three estimators: precision, AUP and AUC are used to evaluate the accuracy of link prediction algorithms. Experimental results on ten tested networks drawn from various fields show that our new index is more effective in predicting missing links than CAR index, especially for networks with low correlation between number of common-neighbors and number of links between common-neighbors.

Discovering Typed Communities in Mobile Social Networks

Mobile social networks, which consist of mobile users who communicate with each other using cell phones, are reflections of people’s interactions in social lives. Discovering typed communities (e.g., family communities or corporate communities) in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling: one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.

A fast and reasonable method for community detection with adjustable extent of overlapping

Communities exist in complex networks of different areas, and in some cases they may overlap between each other. Community detection is a good way to understand the structure, function and evolution of complex networks. There have been some methods to find disjoint or overlapping communities. While most of these methods only fit one single situation, disjoint or overlapping. In our opinion, it is unreasonable to find disjoint communities on a network with clear overlap or to find overlapping communities on a network without any visible overlapping node. In this paper, we propose a link partition based method which can find communities with adjustable extent of overlapping according to backgrounds of specific applications or personal preferences. Experimental results on some real-world networks show that our method can find reasonable communities with adjustable extent of overlapping, and is suitable for networks with high densities and large scales.

A community-based pseudolikelihood approach for relationship labeling in social networks

A social network consists of people (or other social entities) connected by a set of social relationships. Awareness of the relationship types is very helpful for us to understand the structure and the characteristics of the social network. Traditional classifiers are not accurate enough for relationship labeling since they assume that all the labels are independent and identically distributed. A relational probabilistic model, relational Markov networks (RMNs), is introduced to labeling relationships, but the inefficient parameter estimation makes it difficult to deploy in large-scale social networks. In this paper, we propose a communitybased pseudolikelihood (CBPL) approach for relationship labeling. The community structure of a social network is used to assist in constructing the conditional random field, and this makes our approach reasonable and accurate. In addition, the computational simplicity of pseudolikelihood effectively resolves the time complexity problem which RMNs are suffering. We apply our approach on two real-world social networks, one is a terrorist relation network and the other is a phone call network we collected from encrypted call detail records. In our experiments, for avoiding losing links while splitting a closely connected social network into separate training and test subsets, we split the datasets according to the links rather than the individuals. The experimental results show that our approach performs well in terms of accuracy and efficiency.

Predicting top-L missing links with node and link clustering information in large-scale networks

Networks are mathematical structures that are universally used to describe a large variety of complex systems, such as social, biological, and technological systems. The prediction of missing links in incomplete complex networks aims to estimate the likelihood of the existence of a link between a pair of nodes. Various topological features of networks have been applied to develop link prediction methods. However, the exploration of features of links is still limited. In this paper, we demonstrate the power of node and link clustering information in predicting top -L missing links. In the existing literature, link prediction algorithms have only been tested on small-scale and middle-scale networks. The network scale factor has not attracted the same level of attention. In our experiments, we test the proposed method on three groups of networks. For small-scale networks, since the structures are not very complex, advanced methods cannot perform significantly better than classical methods. For middle-scale networks, the proposed index, combining both node and link clustering information, starts to demonstrate its advantages. In many networks, combining both node and link clustering information can improve the link prediction accuracy a great deal. Large-scale networks with more than 100 000 links have rarely been tested previously. Our experiments on three large-scale networks show that local clustering information based methods outperform other methods, and link clustering information can further improve the accuracy of node clustering information based methods, in particular for networks with a broad distribution of the link clustering coefficient.

Spatio-Temporal Recurrent Convolutional Networks for Citywide Short-term Crowd Flows Prediction

With the rapid development of urban traffic, forecasting the flows of crowd plays an increasingly important role in traffic management and public safety. However, it is very challenging as it is affected by many complex factors, including spatio-temporal dependencies of regions and other external factors such as weather and holiday. In this paper, we proposed a deep-learning-based approach, named STRCNs, to forecast both inflow and outflow of crowds in every region of a city. STRCNs combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network structures to capture spatio-temporal dependencies, simultaneously. More particularly, our model can be decomposed into four components: Closeness captures the changes of instantaneous flows; Daily influence detects the changes of daily influence flows regularly; Weekly influence reacts weekly patterns of influence flows and External influence gets the influence of external factors. For the first three properties (Closeness, Daily influence and Weekly influence), we give a branch of recurrent convolutional network units to learn both spatial and temporal dependencies in crowd flows. External factors are fed into a two-layers fully connected neural network. STRCNs assigns different weights to different branches, and then merges the outputs of the four parts together. Experimental results on two data sets (MobileBJ and TaxiBJ) demonstrate that STRCNs outperforms classical time series and other deep-learning-based prediction methods.