Temporal　lobe　epilepsy　(TLE)　is　considered　a　network　disorder　rather　than　a　localized　lesion,　making　it　essential　to　study　the　network　mechanisms　underlying　TLE.　In　this　study,　we　constructed　directed　brain　networks　based　on　clinical　MEG　data　using　the　Granger　Causality　Analysis　(GCA)　method,　aiming　to　provide　new　insights　into　the　network　mechanisms　of　TLE.　MEG　data　from　13　lTLE　and　21　rTLE　patients　and　14　healthy　controls　(HCs)　were　analyzed.　The　preprocessed　MEG　data　were　used　to　construct　directed　brain　networks　using　the　GCA　method　and　undirected　brain　networks　using　the　Pearson　Correlation　Coefficient　(PCC)　method.　Graph　theoretical　analysis　extracted　global　and　local　topologies　from　the　binary　matrix,　and　SVM　classified　topologies　with　significant　differences　(p　＜　0.05).　Comparative　studies　were　performed　on　connectivity　strengths,　graph　theory　metrics,　and　SVM　classifications　between　GCA　and　PCC,　with　an　additional　analysis　of　GCA-weighted　network　connectivity.　The　results　show　that　TLE　patients　showed　significantly　increased　functional　connectivity　based　on　GCA　compared　to　the　control　group;　similarities　of　the　hub　brain　regions　between　lTLE　and　rTLE　patients　and　the　cortical-limbic-thalamic-cortical　loop　were　identified;　TLE　patients　exhibited　a　significant　increase　in　GCA-based　Global　Clustering　Coefficient　(GCC)　and　Global　Local　Efficiency　(GLE);　most　brain　regions　with　abnormal　local　topological　properties　in　TLE　patients　overlapped　with　their　hub　regions.　The　directionality　of　brain　connectivity　has　played　a　significantly　more　pivotal　role　in　research　on　TLE.　GCA　may　be　a　potential　tool　in　MEG　analysis　to　distinguish　TLE　patients　and　HC　effectively.