TopCom: Index for Shortest Distance Query in Directed Graph

Finding shortest distance between two vertices in a graph is an important problem due to its numerous applications in diverse domains, including geo-spatial databases, social network analysis, and information retrieval. Classical algorithms (such as, Dijkstra) solve this problem in polynomial time, but these algorithms cannot provide real-time response for a large number of bursty queries on a large graph. So, indexing based solutions that pre-process the graph for efficiently answering (exactly or approximately) a large number of distance queries in real-time is becoming increasingly popular. Existing solutions have varying performance in terms of index size, index building time, query time, and accuracy. In this work, we propose T OP C OM , a novel indexing-based solution for exactly answering distance queries. Our experiments with two of the existing state-of-the-art methods (IS-Label and TreeMap) show the superiority of T OP C OM over these two methods considering scalability and query time. Besides, indexing of T OP C OM exploits the DAG (directed acyclic graph) structure in the graph, which makes it significantly faster than the existing methods if the SCCs (strongly connected component) of the input graph are relatively small

Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

Forecasting the Daily Dynamic Hedge Ratios in Emerging European Stock Futures Markets: Evidence from GARCH models

This paper empirically estimates and forecasts the hedge ratios of three emerging European and one developed stock futures markets by means of seven different versions of GARCH model. The seven GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-DCC, GARCH-X, GARCH-GJR and GARCH-JUMP. Daily data during January 2000-July 2014 from Greece, Hungary, Poland and the UK are applied. Forecast errors based on these four stock futures portfolio return forecasts (based on forecasted hedge ratios) are employed to evaluate out-of-sample forecasting ability of the seven GARCH models. The comparison is done by means of Model Confidence Set (MCS) and modified Diebold-Mariano tests. Forecasts are conducted over two nonoverlapping out-of-sample periods, a two-year period and a one-year period. MCS results indicate that the GARCH model provides the most accurate forecasts in five cases, while each of the GARCH-ECM, GARCH-X and GARCH-GJR models constitutes model confidence set in four cases at a reasonable confidence level. Models selection based on modified Diebold-Mariano tests further corroborate results of the MCS tests. Differences between the portfolio returns also indicate the high forecasting ability of GARCH-BEKK and GARCH-GJR models

E-CLoG: Counting edge-centric local graphlets

In recent years, graphlet counting has emerged as an important task in topological graph analysis. However, the existing works on graphlet counting obtain the graphlet counts for the entire network as a whole. These works capture the key graphical patterns that prevail in a given network but they fail to meet the demand of the majority of real-life graph related prediction tasks such as link prediction, edge/node classification, etc., which require to build features for an edge (or a vertex) of a network. To meet the demand for such applications, efficient algorithms are needed for counting local graphlets within the context of an edge (or a vertex). In this work, we propose an efficient method, titled E-CLOG, for counting all 3,4 and 5 size local graphlets with the context of a given edge for its all different edge orbits. We also provide a shared-memory, multi-core implementation of E-CLOG, which makes it even more scalable for very large real-world networks. In particular, We obtain strong scaling on a variety of graphs (14x-20x on 36 cores). We provide extensive experimental results to demonstrate the efficiency and effectiveness of the proposed method. For instance, we show that E-CLOG is faster than existing work by multiple order of magnitudes; for the Wordnet graph E-CLOG counts all 3,4 and 5-size local graphlets in 1.5 hours using a single thread and in only a few minutes using the parallel implementation, whereas the baseline method does not finish in more than 4 days. We also show that local graphlet counts around an edge are much better features for link prediction than well-known topological features; our experiments show that the former enjoys between 10% to 45% of improvement in the AUC value for predicting future links in three real-life social and collaboration networks

Feature Selection for Classification under Anonymity Constraint

Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously. In fact, security researchers have shown that sparse microdata containing information about online activities of a user although anonymous, can still be used to disclose the identity of the user by cross-referencing the data with other data sources. To preserve the privacy of a user, in existing works several methods (k-anonymity, l-diversity, differential privacy) are proposed that ensure a dataset which is meant to share or publish bears small identity disclosure risk. However, the majority of these methods modify the data in isolation, without considering their utility in subsequent knowledge discovery tasks, which makes these datasets less informative. In this work, we consider labeled data that are generally used for classification, and propose two methods for feature selection considering two goals: first, on the reduced feature set the data has small disclosure risk, and second, the utility of the data is preserved for performing a classification task. Experimental results on various real-world datasets show that the method is effective and useful in practice

An assessment of the contribution of consumer confidence towards household spending decisions using UK data

The European Commission’s consumer confidence indicator (CCI) is assembled from responses to four questions about individual and general economic prospects which form part of the EU’s Consumer Survey. However, concerns may be raised about whether the four components should be constrained to exerting the same influence in a forecasting model of household consumption. Also, in this context, it would seem to be appropriate to permit a role to other information that is obtained from the EU survey. Consequently, in this article, different regression functions are specified in order to assess whether there is any gain to be achieved in predictive accuracy from adopting a more flexible approach towards using the data from the EU questionnaire. With an emphasis upon parsimony, an econometric analysis is performed in conjunction with UK quarterly data on household consumption expenditure. For two categories of spending, it is discovered that the quality of forecasts benefits from having undertaken disaggregation involving survey data beyond those which contribute towards the calculation of the CCI. Indeed, the respective consumption variables (relating to non-durable goods and durable goods excluding vehicles) are seen to be associated with relatively volatile behaviour over the forecast interval, 2008–2013

Comparative Performances of Measures of Consumer and Economic Sentiment in Forecasting Consumption: A Multi-Country Analysis

This paper seeks to compare the capabilities of assorted measures of consumer and economic sentiment in predicting the growth of household expenditure. An analysis of quarterly data on five European countries shows that for none of these can the model which incorporates the EU’s headline consumer confidence indicator be deemed to be significantly inferior to any of its seven rivals. However, the rankings of the sentiment variables are seen to be influenced by: the proportion of total spending by households that is devoted to durable goods; and the nature of the behaviour of consumption over the forecast interval