Pattern Migration Visualisation Summary

The Pattern Migration Visualisation module provided further analytical support to allow users to investigate pattern migrations between SOM maps that have been gen- erated using the Trend Grouping module. Using the animation facility, the migration of

patterns can be illustrated and changes of trend type associated with temporal patterns highlighted. The above discussion, focusing on the migration of particular patterns, in- dicates that the proposed visualisation mechanism provides a useful tool for decision makers.

5.5

Summary

In this chapter, results from a number of experiments undertaken to analyse the Fre- quent Pattern Trend Analysis element of the proposed framework have been reported. The analysis was considered in terms of the Trend Identification, Trend Grouping, Pattern Migration Clustering and Pattern Migration Visualisation modules. The ex- periments were conducted using three social networks: (i) CTS, (ii) Deeside Insurance and (iii) MAF Logistic Cargo networks.

The analysis of the Trend Identification module showed that a large number of fre- quent patterns and trends are discovered using the TM-TFP algorithm tending their interpretation to be difficult. The discovered trends are thus grouped using the Trend Grouping module, based on SOM technology. The module generated prototype maps and trend line maps to classify the types of trends that exist for all discovered patterns. From this experimental analysis, the Trend Identification and Grouping modules high- lighted interesting information which was conjectured to be beneficial to decision mak- ers. The proposed use of constraints further assisted decision makers in that it allowed them to “focus in” on particular types (clusters) of trends. The Pattern Migration Clustering and Pattern Migration Visualisation modules provided additional support for the analysis of the network data. The evaluations conducted with respect to the Pattern Migration Clustering module indicated that it provided for the identification of pattern migrations between trend clusters and pattern migration communities. The Pattern Migration Visualisation module then allowed users to view pattern migrations between pairs of SOM maps. The animation facility included in the visualisation mod- ule allowed for the demonstration of how trend configurations change with time.

In the following chapter, the prediction element of the proposed framework is pre- sented to illustrate how frequent patterns (for example information or events) may be predicted to “travel” across a network. The prediction modules use the patterns and trends discovered by the Trend Identification module evaluated in this chapter.

Chapter 6

Prediction Modeling

Using modern ICT infrastructures social networks may change rapidly. The static “snap shot” node and link model of a social network describes the structure of a network and gives an indication of how information moves across the network (both directly and indirectly) at a given instance of time. However, such static analysis does not necessarily present a “true” picture. The proposed mechanisms described in the previous chapter to support dynamic analysis of networks can be argued to go some way to presenting a better picture. The work described in this chapter extends the capabilities provided by the mechanisms described in the foregoing chapter. Regardless of the type of social network under consideration (online social network, business community, file sharing systems, co-authoring framework, etc) the prediction of how an activity or event may spread across a network can clearly provide useful information with respect to many applications.

This chapter describes how the frequent patterns and trends discovered using the previous described modules may be used for prediction modeling. The work described in this chapter is motivated by a desire to use the discovered patterns and trends to predict the “percolation” of activities in networks. The work is also influenced by the concept of causal chains in networks [107, 114] which in turn suggests the use of the trends associated with identified frequent patterns as probabilistic indicators with which to determine the frequency of traffic percolating across a network.

This chapter thus presents the second part of the proposed Predictive Trend Min- ing Framework (PTMF). This second part comprises two modules: (i) the Percolation Matrix module and (ii) the Visualisation module. Collectively these two modules are referred to as the Prediction Modeling (PM) modules. The Percolation Matrix module operates as follows: (i) filter a set of frequent patterns of interest, and (ii) calculate the probabilities of information or events traveling from one node to another. The Visual- isation module is used to illustrates the result from the Percolation Matrix module in the form ofprobability mapsgenerated using a further extension of the Visuset software

system coupled with Google Earth1; the latter is so as to present the probability maps in the context of geographical locations. These two modules were incorporated into the framework. A drill down mechanism is also proposed so that users can focus their investigation of how information percolates across a selected subset of nodes in a given network. The conceptualisation and nature of both modules, and the associated evalu- ation, are described in detail in this chapter. The evaluation was again conducted using the GB cattle movement dataset that forms the central element of the Cattle Tracing System (CTS) in operation in England, Wales and Scotland. The CTS network was selected because: (i) it is the largest dataset considered in this thesis, and (ii) it was envisaged that the nature of its complex star form (as described in Chapter 3) would provide more interesting probability maps.

The rest of this chapter is organized as follows. In Section 6.1 some background on types of patterns that are required for use with the proposed prediction modeling is presented. Section 6.2 discusses the Percolation Matrix in detail. Then Section 6.3 describes the visualization module. In Section 6.4 the application of the “drill down” mechanism, that allows users to focus on a specific group of patterns based on their spatial attributes, is presented. Section 6.5 presents the results from the experimental analysis of the two modules that make up the prediction element of the PTMF. Finally, in Section 6.6 the chapter is concluded with a brief summary, some discussion and conclusions.

6.1

Background

The proposed prediction modeling mechanism is founded on the frequent patterns and trends generated using the TM-TFP algorithm in the Trend Identification module. As described in Chapter 4, the frequent pattern trends are identified from the analysis of a sequence of social network datasets. Recall that each frequent pattern trend is described in terms of its temporal occurrence counts (support values). This section comprises two sub-sections. Sub-section 6.1.1 defines the nature of the patterns that may be processed using the PM modules. Sub-section 6.1.2 presents an overview of the proposed PM process.