Prediction and clustering in signed networks: a local to global perspective
نویسندگان
چکیده
The study of social networks is a burgeoning research area. However, most existing work is on networks that simply encode whether relationships exist or not. In contrast, relationships in signed networks can be positive (“like”, “trust”) or negative (“dislike”, “distrust”). The theory of social balance shows that signed networks tend to conform to some local patterns that, in turn, induce certain global characteristics. In this paper, we exploit both local as well as global aspects of social balance theory for two fundamental problems in the analysis of signed networks: sign prediction and clustering. Local patterns of social balance have been used in the past for sign prediction. We define more general measures of social imbalance (MOIs) based on `-cycles in the network and give a simple sign prediction rule. Interestingly, by examining measures of social imbalance, we show that the classic Katz measure, which is used widely in unsigned link prediction, also has a balance theoretic interpretation when applied to signed networks. Motivated by the global structure of balanced networks, we propose an effective low rank modeling approach for both sign prediction and clustering. We provide theoretical performance guarantees for our low-rank matrix completion approach via convex relaxations, scale it up to large problem sizes using a matrix factorization based algorithm, and provide extensive experimental validation including comparisons with local approaches. Our experimental results indicate that, by adopting a more global viewpoint of social balance, we get significant performance and computational gains in prediction and clustering tasks on signed networks. Our work therefore highlights the usefulness of the global aspect of balance theory for the analysis of signed networks.
منابع مشابه
Link Prediction using Network Embedding based on Global Similarity
Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...
متن کاملPrediction-Based Portfolio Optimization Model for Iran’s Oil Dependent Stocks Using Data Mining Methods
This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...
متن کاملImprovement of Routing Operation Based on Learning with Using Smart Local and Global Agents and with the Help of the Ant Colony Algorithm
Routing in computer networks has played a special role in recent years. The cause of this is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider p...
متن کاملImprovement of Routing Operation Based on Learning with Using Smart Local and Global Agents and with the Help of the Ant Colony Algorithm
Routing in computer networks has played a special role in recent years. The cause of this is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider p...
متن کاملSpectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of Machine Learning Research
دوره 15 شماره
صفحات -
تاریخ انتشار 2014