Studies　on　graph　contrastive　learning,　which　is　an　effective　way　of　self-supervision,　have　achieved　excellent　experimental　performance.　Most　existing　methods　generate　two　augmented　views,　and　then　perform　feature　learning　on　the　two　views　through　maximizing　semantic　consistency.　Nevertheless,　it　is　still　challenging　to　generate　optimal　views　to　facilitate　the　graph　construction　that　can　reveal　the　essential　association　relations　among　nodes　by　graph　contrastive　learning.　Considering　that　the　extremely　high　mutual　information　between　views　is　prone　to　have　a　negative　effect　on　model　training,　a　good　choice　is　to　add　constraints　to　the　graph　data　augmentation　process.　This　paper　proposes　two　constraint　principles,　low　dissimilarity　priority　(LDP)　and　mutual　exclusion　(ME),　to　mitigate　the　mutual　information　between　views　and　compress　redundant　parts　of　mutual　information　between　views.　LDP　principle　aims　to　reduce　the　mutual　information　between　views　at　global　scale,　and　ME　principle　works　to　reduce　the　mutual　information　at　local　scale.　They　are　opposite　and　appropriate　in　different　situations.　Without　loss　of　generality,　the　two　proposed　principles　are　applied　to　two　well-performed　graph　contrastive　methods,　i.e.　GraphCL　and　GCA,　and　experimental　results　on　20　public　benchmark　datasets　show　that　the　models　with　the　aid　of　the　two　proposed　constraint　principles　achieve　higher　recognition　accuracy.